#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
// This file contains all native_functions that can be registered to
// and the schema string that they should be registered with

at::Tensor _cast_Byte(const at::Tensor & self, bool non_blocking); // {"schema": "aten::_cast_Byte(Tensor self, bool non_blocking=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _cast_Char(const at::Tensor & self, bool non_blocking); // {"schema": "aten::_cast_Char(Tensor self, bool non_blocking=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _cast_Double(const at::Tensor & self, bool non_blocking); // {"schema": "aten::_cast_Double(Tensor self, bool non_blocking=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _cast_Float(const at::Tensor & self, bool non_blocking); // {"schema": "aten::_cast_Float(Tensor self, bool non_blocking=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _cast_Int(const at::Tensor & self, bool non_blocking); // {"schema": "aten::_cast_Int(Tensor self, bool non_blocking=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _cast_Long(const at::Tensor & self, bool non_blocking); // {"schema": "aten::_cast_Long(Tensor self, bool non_blocking=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _cast_Short(const at::Tensor & self, bool non_blocking); // {"schema": "aten::_cast_Short(Tensor self, bool non_blocking=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _cast_Half(const at::Tensor & self, bool non_blocking); // {"schema": "aten::_cast_Half(Tensor self, bool non_blocking=False) -> Tensor", "dispatch": "False", "default": "True"}
void _backward(const at::Tensor & self, at::TensorList inputs, const ::std::optional<at::Tensor> & gradient, ::std::optional<bool> retain_graph, bool create_graph); // {"schema": "aten::_backward(Tensor self, Tensor[] inputs, Tensor? gradient=None, bool? retain_graph=None, bool create_graph=False) -> ()", "dispatch": "False", "default": "True"}
void set_data(at::Tensor & self, const at::Tensor & new_data); // {"schema": "aten::set_data(Tensor(a!) self, Tensor new_data) -> ()", "dispatch": "False", "default": "True"}
at::Tensor data(const at::Tensor & self); // {"schema": "aten::data(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
bool is_leaf(const at::Tensor & self); // {"schema": "aten::is_leaf(Tensor self) -> bool", "dispatch": "False", "default": "True"}
int64_t output_nr(const at::Tensor & self); // {"schema": "aten::output_nr(Tensor self) -> int", "dispatch": "False", "default": "True"}
int64_t _version(const at::Tensor & self); // {"schema": "aten::_version(Tensor self) -> int", "dispatch": "False", "default": "True"}
at::Tensor & requires_grad_(at::Tensor & self, bool requires_grad); // {"schema": "aten::requires_grad_(Tensor(a!) self, bool requires_grad=True) -> Tensor(a!)", "dispatch": "False", "default": "True"}
void retain_grad(at::Tensor & self); // {"schema": "aten::retain_grad(Tensor(a!) self) -> ()", "dispatch": "False", "default": "True"}
bool retains_grad(const at::Tensor & self); // {"schema": "aten::retains_grad(Tensor self) -> bool", "dispatch": "False", "default": "True"}
at::Tensor _fw_primal(const at::Tensor & self, int64_t level); // {"schema": "aten::_fw_primal(Tensor(a) self, int level) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor _make_dual(const at::Tensor & primal, const at::Tensor & tangent, int64_t level); // {"schema": "aten::_make_dual(Tensor(a) primal, Tensor tangent, int level) -> Tensor(a)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> _unpack_dual(const at::Tensor & dual, int64_t level); // {"schema": "aten::_unpack_dual(Tensor(a) dual, int level) -> (Tensor(a) primal, Tensor tangent)", "dispatch": "False", "default": "True"}
at::Tensor _new_zeros_with_same_feature_meta(const at::Tensor & self, const at::Tensor & other, int64_t self_num_batch_dims); // {"schema": "aten::_new_zeros_with_same_feature_meta(Tensor self, Tensor other, *, int self_num_batch_dims=0) -> Tensor", "dispatch": "True", "default": "True"}
bool _has_same_storage_numel(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::_has_same_storage_numel(Tensor self, Tensor other) -> bool", "dispatch": "True", "default": "True"}
void _assert_async(const at::Tensor & self); // {"schema": "aten::_assert_async(Tensor self) -> ()", "dispatch": "True", "default": "False"}
void _assert_async(const at::Tensor & self, c10::string_view assert_msg); // {"schema": "aten::_assert_async.msg(Tensor self, str assert_msg) -> ()", "dispatch": "True", "default": "False"}
void _assert_scalar(const at::Scalar & self, c10::string_view assert_msg); // {"schema": "aten::_assert_scalar(Scalar self, str assert_msg) -> ()", "dispatch": "True", "default": "True"}
at::Tensor _functional_assert_scalar(const at::Scalar & self, c10::string_view assert_msg, const at::Tensor & dep_token); // {"schema": "aten::_functional_assert_scalar(Scalar self, str assert_msg, Tensor dep_token) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _functional_assert_async(const at::Tensor & self, c10::string_view assert_msg, const at::Tensor & dep_token); // {"schema": "aten::_functional_assert_async.msg(Tensor self, str assert_msg, Tensor dep_token) -> Tensor", "dispatch": "True", "default": "False"}
void _assert_tensor_metadata(const at::Tensor & a, at::OptionalSymIntArrayRef size, at::OptionalSymIntArrayRef stride, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Device> device, ::std::optional<at::Layout> layout); // {"schema": "aten::_assert_tensor_metadata(Tensor a, SymInt[]? size=None, SymInt[]? stride=None, ScalarType? dtype=None, *, Device? device=None, Layout? layout=None) -> ()", "dispatch": "True", "default": "True"}
void _print(c10::string_view s); // {"schema": "aten::_print(str s) -> ()", "dispatch": "True", "default": "True"}
void sym_constrain_range(const at::Scalar & size, ::std::optional<int64_t> min, ::std::optional<int64_t> max); // {"schema": "aten::sym_constrain_range(Scalar size, *, int? min=None, int? max=None) -> ()", "dispatch": "True", "default": "True"}
void sym_constrain_range_for_size(const at::Scalar & size, ::std::optional<int64_t> min, ::std::optional<int64_t> max); // {"schema": "aten::sym_constrain_range_for_size(Scalar size, *, int? min=None, int? max=None) -> ()", "dispatch": "True", "default": "True"}
at::Tensor _functional_sym_constrain_range(const at::Scalar & size, ::std::optional<int64_t> min, ::std::optional<int64_t> max, const at::Tensor & dep_token); // {"schema": "aten::_functional_sym_constrain_range(Scalar size, int? min, int? max, Tensor dep_token) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _functional_sym_constrain_range_for_size(const at::Scalar & size, ::std::optional<int64_t> min, ::std::optional<int64_t> max, const at::Tensor & dep_token); // {"schema": "aten::_functional_sym_constrain_range_for_size(Scalar size, int? min, int? max, Tensor dep_token) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _make_dep_token(::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::_make_dep_token(*, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "True", "default": "False"}
bool _use_cudnn_ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank); // {"schema": "aten::_use_cudnn_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank) -> bool", "dispatch": "True", "default": "False"}
bool _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); // {"schema": "aten::_use_cudnn_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank) -> bool", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _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); // {"schema": "aten::_cudnn_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank, bool deterministic, bool zero_infinity) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _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); // {"schema": "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)", "dispatch": "True", "default": "False"}
bool _use_cudnn_rnn_flatten_weight(); // {"schema": "aten::_use_cudnn_rnn_flatten_weight() -> bool", "dispatch": "False", "default": "True"}
at::Tensor _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); // {"schema": "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", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _cudnn_rnn(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const ::std::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & 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<at::Tensor> & dropout_state); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,::std::vector<at::Tensor>> _cudnn_rnn_backward(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, 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<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask); // {"schema": "aten::_cudnn_rnn_backward(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, 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 reserve, bool[4] output_mask) -> (Tensor, Tensor, Tensor, Tensor[])", "dispatch": "True", "default": "False"}
at::Tensor _cudnn_init_dropout_state(double dropout, bool train, int64_t dropout_seed, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "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", "dispatch": "True", "default": "False"}
int64_t _debug_has_internal_overlap(const at::Tensor & self); // {"schema": "aten::_debug_has_internal_overlap(Tensor self) -> int", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> _fused_dropout(const at::Tensor & self, double p, ::std::optional<at::Generator> generator); // {"schema": "aten::_fused_dropout(Tensor self, float p, Generator? generator=None) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor _masked_scale(const at::Tensor & self, const at::Tensor & mask, double scale); // {"schema": "aten::_masked_scale(Tensor self, Tensor mask, float scale) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> native_dropout(const at::Tensor & input, double p, ::std::optional<bool> train); // {"schema": "aten::native_dropout(Tensor input, float p, bool? train) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor native_dropout_backward(const at::Tensor & grad_output, const at::Tensor & mask, double scale); // {"schema": "aten::native_dropout_backward(Tensor grad_output, Tensor mask, float scale) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _sobol_engine_draw(const at::Tensor & quasi, int64_t n, const at::Tensor & sobolstate, int64_t dimension, int64_t num_generated, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::_sobol_engine_draw(Tensor quasi, int n, Tensor sobolstate, int dimension, int num_generated, ScalarType? dtype) -> (Tensor, Tensor)", "dispatch": "False", "default": "True"}
at::Tensor & _sobol_engine_ff_(at::Tensor & self, int64_t n, const at::Tensor & sobolstate, int64_t dimension, int64_t num_generated); // {"schema": "aten::_sobol_engine_ff_(Tensor(a!) self, int n, Tensor sobolstate, int dimension, int num_generated) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & _sobol_engine_scramble_(at::Tensor & self, const at::Tensor & ltm, int64_t dimension); // {"schema": "aten::_sobol_engine_scramble_(Tensor(a!) self, Tensor ltm, int dimension) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & _sobol_engine_initialize_state_(at::Tensor & self, int64_t dimension); // {"schema": "aten::_sobol_engine_initialize_state_(Tensor(a!) self, int dimension) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor _reshape_from_tensor(const at::Tensor & self, const at::Tensor & shape); // {"schema": "aten::_reshape_from_tensor(Tensor self, Tensor shape) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _shape_as_tensor(const at::Tensor & self); // {"schema": "aten::_shape_as_tensor(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor dropout(const at::Tensor & input, double p, bool train); // {"schema": "aten::dropout(Tensor input, float p, bool train) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & dropout_(at::Tensor & self, double p, bool train); // {"schema": "aten::dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor feature_dropout(const at::Tensor & input, double p, bool train); // {"schema": "aten::feature_dropout(Tensor input, float p, bool train) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & feature_dropout_(at::Tensor & self, double p, bool train); // {"schema": "aten::feature_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor alpha_dropout(const at::Tensor & input, double p, bool train); // {"schema": "aten::alpha_dropout(Tensor input, float p, bool train) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & alpha_dropout_(at::Tensor & self, double p, bool train); // {"schema": "aten::alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor feature_alpha_dropout(const at::Tensor & input, double p, bool train); // {"schema": "aten::feature_alpha_dropout(Tensor input, float p, bool train) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & feature_alpha_dropout_(at::Tensor & self, double p, bool train); // {"schema": "aten::feature_alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor abs(const at::Tensor & self); // {"schema": "aten::abs(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & abs_(at::Tensor & self); // {"schema": "aten::abs_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & abs_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor absolute(const at::Tensor & self); // {"schema": "aten::absolute(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & absolute_(at::Tensor & self); // {"schema": "aten::absolute_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & absolute_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::absolute.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor angle(const at::Tensor & self); // {"schema": "aten::angle(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & angle_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::angle.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor view_as_real(const at::Tensor & self); // {"schema": "aten::view_as_real(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "False"}
at::Tensor view_as_complex(const at::Tensor & self); // {"schema": "aten::view_as_complex(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "False"}
at::Tensor sgn(const at::Tensor & self); // {"schema": "aten::sgn(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & sgn_(at::Tensor & self); // {"schema": "aten::sgn_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & sgn_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::sgn.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor chalf(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::chalf(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor real(const at::Tensor & self); // {"schema": "aten::real(Tensor(a) self) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor imag(const at::Tensor & self); // {"schema": "aten::imag(Tensor(a) self) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor _conj(const at::Tensor & self); // {"schema": "aten::_conj(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor conj(const at::Tensor & self); // {"schema": "aten::conj(Tensor(a) self) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor _conj_physical(const at::Tensor & self); // {"schema": "aten::_conj_physical(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor conj_physical(const at::Tensor & self); // {"schema": "aten::conj_physical(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & conj_physical_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::conj_physical.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & conj_physical_(at::Tensor & self); // {"schema": "aten::conj_physical_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor resolve_conj(const at::Tensor & self); // {"schema": "aten::resolve_conj(Tensor(a) self) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor resolve_neg(const at::Tensor & self); // {"schema": "aten::resolve_neg(Tensor(a) self) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor _neg_view(const at::Tensor & self); // {"schema": "aten::_neg_view(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor acos(const at::Tensor & self); // {"schema": "aten::acos(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & acos_(at::Tensor & self); // {"schema": "aten::acos_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & acos_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::acos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor arccos(const at::Tensor & self); // {"schema": "aten::arccos(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & arccos_(at::Tensor & self); // {"schema": "aten::arccos_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & arccos_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::arccos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor avg_pool1d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor adaptive_avg_pool1d(const at::Tensor & self, at::IntArrayRef output_size); // {"schema": "aten::adaptive_avg_pool1d(Tensor self, int[1] output_size) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> adaptive_max_pool1d(const at::Tensor & self, at::IntArrayRef output_size); // {"schema": "aten::adaptive_max_pool1d(Tensor self, int[1] output_size) -> (Tensor, Tensor)", "dispatch": "False", "default": "True"}
at::Tensor add(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha); // {"schema": "aten::add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & add_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha); // {"schema": "aten::add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & add_out(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _add_relu(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha); // {"schema": "aten::_add_relu.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & _add_relu_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha); // {"schema": "aten::_add_relu_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & _add_relu_out(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::_add_relu.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _add_relu(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha); // {"schema": "aten::_add_relu.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & _add_relu_(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha); // {"schema": "aten::_add_relu_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor add(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha); // {"schema": "aten::add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & add_(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha); // {"schema": "aten::add_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor addmv(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha); // {"schema": "aten::addmv(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & addmv_(at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha); // {"schema": "aten::addmv_(Tensor(a!) self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & addmv_out(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::addmv.out(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor addr(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha); // {"schema": "aten::addr(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & addr_(at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha); // {"schema": "aten::addr_(Tensor(a!) self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & addr_out(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor affine_grid_generator(const at::Tensor & theta, c10::SymIntArrayRef size, bool align_corners); // {"schema": "aten::affine_grid_generator(Tensor theta, SymInt[] size, bool align_corners) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor affine_grid_generator_backward(const at::Tensor & grad, c10::SymIntArrayRef size, bool align_corners); // {"schema": "aten::affine_grid_generator_backward(Tensor grad, SymInt[] size, bool align_corners) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _is_all_true(const at::Tensor & self); // {"schema": "aten::_is_all_true(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _is_any_true(const at::Tensor & self); // {"schema": "aten::_is_any_true(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _test_check_tensor(const at::Tensor & self); // {"schema": "aten::_test_check_tensor(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _test_functorch_fallback(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::_test_functorch_fallback(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor all(const at::Tensor & self, int64_t dim, bool keepdim); // {"schema": "aten::all.dim(Tensor self, int dim, bool keepdim=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor all(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim); // {"schema": "aten::all.dims(Tensor self, int[]? dim=None, bool keepdim=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & all_out(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & out); // {"schema": "aten::all.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & all_out(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, at::Tensor & out); // {"schema": "aten::all.dims_out(Tensor self, int[]? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
bool allclose(const at::Tensor & self, const at::Tensor & other, double rtol, double atol, bool equal_nan); // {"schema": "aten::allclose(Tensor self, Tensor other, float rtol=1e-05, float atol=1e-08, bool equal_nan=False) -> bool", "dispatch": "True", "default": "True"}
at::Tensor any(const at::Tensor & self, int64_t dim, bool keepdim); // {"schema": "aten::any.dim(Tensor self, int dim, bool keepdim=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor any(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim); // {"schema": "aten::any.dims(Tensor self, int[]? dim=None, bool keepdim=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & any_out(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & out); // {"schema": "aten::any.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & any_out(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, at::Tensor & out); // {"schema": "aten::any.dims_out(Tensor self, int[]? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor arange(const at::Scalar & end, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::arange(Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor arange(const at::Scalar & start, const at::Scalar & end, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor arange(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "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", "dispatch": "True", "default": "True"}
at::Tensor & arange_out(const at::Scalar & end, at::Tensor & out); // {"schema": "aten::arange.out(Scalar end, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & arange_out(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::Tensor & out); // {"schema": "aten::arange.start_out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _dim_arange(const at::Tensor & like, int64_t dim); // {"schema": "aten::_dim_arange(Tensor like, int dim) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor argmax(const at::Tensor & self, ::std::optional<int64_t> dim, bool keepdim); // {"schema": "aten::argmax(Tensor self, int? dim=None, bool keepdim=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & argmax_out(const at::Tensor & self, ::std::optional<int64_t> dim, bool keepdim, at::Tensor & out); // {"schema": "aten::argmax.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor argmin(const at::Tensor & self, ::std::optional<int64_t> dim, bool keepdim); // {"schema": "aten::argmin(Tensor self, int? dim=None, bool keepdim=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & argmin_out(const at::Tensor & self, ::std::optional<int64_t> dim, bool keepdim, at::Tensor & out); // {"schema": "aten::argmin.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor acosh(const at::Tensor & self); // {"schema": "aten::acosh(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & acosh_(at::Tensor & self); // {"schema": "aten::acosh_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & acosh_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::acosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor arccosh(const at::Tensor & self); // {"schema": "aten::arccosh(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & arccosh_(at::Tensor & self); // {"schema": "aten::arccosh_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & arccosh_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::arccosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor asinh(const at::Tensor & self); // {"schema": "aten::asinh(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & asinh_(at::Tensor & self); // {"schema": "aten::asinh_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & asinh_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::asinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor arcsinh(const at::Tensor & self); // {"schema": "aten::arcsinh(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & arcsinh_(at::Tensor & self); // {"schema": "aten::arcsinh_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & arcsinh_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::arcsinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor atanh(const at::Tensor & self); // {"schema": "aten::atanh(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & atanh_(at::Tensor & self); // {"schema": "aten::atanh_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & atanh_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::atanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor arctanh(const at::Tensor & self); // {"schema": "aten::arctanh(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & arctanh_(at::Tensor & self); // {"schema": "aten::arctanh_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & arctanh_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::arctanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor as_strided(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset); // {"schema": "aten::as_strided(Tensor(a) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a)", "dispatch": "True", "default": "False"}
const at::Tensor & as_strided_(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset); // {"schema": "aten::as_strided_(Tensor(a!) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor asin(const at::Tensor & self); // {"schema": "aten::asin(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & asin_(at::Tensor & self); // {"schema": "aten::asin_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & asin_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::asin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor arcsin(const at::Tensor & self); // {"schema": "aten::arcsin(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & arcsin_(at::Tensor & self); // {"schema": "aten::arcsin_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & arcsin_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::arcsin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor atan(const at::Tensor & self); // {"schema": "aten::atan(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & atan_(at::Tensor & self); // {"schema": "aten::atan_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & atan_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor arctan(const at::Tensor & self); // {"schema": "aten::arctan(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & arctan_(at::Tensor & self); // {"schema": "aten::arctan_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & arctan_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::arctan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor atleast_1d(const at::Tensor & self); // {"schema": "aten::atleast_1d(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> atleast_1d(at::TensorList tensors); // {"schema": "aten::atleast_1d.Sequence(Tensor[] tensors) -> Tensor[]", "dispatch": "False", "default": "True"}
at::Tensor atleast_2d(const at::Tensor & self); // {"schema": "aten::atleast_2d(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> atleast_2d(at::TensorList tensors); // {"schema": "aten::atleast_2d.Sequence(Tensor[] tensors) -> Tensor[]", "dispatch": "False", "default": "True"}
at::Tensor atleast_3d(const at::Tensor & self); // {"schema": "aten::atleast_3d(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> atleast_3d(at::TensorList tensors); // {"schema": "aten::atleast_3d.Sequence(Tensor[] tensors) -> Tensor[]", "dispatch": "False", "default": "True"}
at::Tensor baddbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha); // {"schema": "aten::baddbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & baddbmm_(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha); // {"schema": "aten::baddbmm_(Tensor(a!) self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & baddbmm_out(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::baddbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor baddbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::ScalarType out_dtype, const at::Scalar & beta, const at::Scalar & alpha); // {"schema": "aten::baddbmm.dtype(Tensor self, Tensor batch1, Tensor batch2, ScalarType out_dtype, *, Scalar beta=1, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & baddbmm_out(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, at::ScalarType out_dtype, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::baddbmm.dtype_out(Tensor self, Tensor batch1, Tensor batch2, ScalarType out_dtype, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor bartlett_window(int64_t window_length, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::bartlett_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor bartlett_window(int64_t window_length, bool periodic, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::bartlett_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor batch_norm(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double momentum, double eps, bool cudnn_enabled); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor quantized_batch_norm(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & mean, const at::Tensor & var, double eps, double output_scale, int64_t output_zero_point); // {"schema": "aten::quantized_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,int64_t> _batch_norm_impl_index(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double momentum, double eps, bool cudnn_enabled); // {"schema": "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)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _batch_norm_impl_index_backward(int64_t impl_index, const at::Tensor & input, const at::Tensor & grad_output, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_var_transform, bool train, double eps, ::std::array<bool,3> output_mask, const at::Tensor & reservedSpace); // {"schema": "aten::_batch_norm_impl_index_backward(int impl_index, Tensor input, Tensor grad_output, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var_transform, bool train, float eps, bool[3] output_mask, Tensor reservedSpace) -> (Tensor, Tensor, Tensor)", "dispatch": "False", "default": "True"}
at::Tensor bernoulli(const at::Tensor & self, ::std::optional<at::Generator> generator); // {"schema": "aten::bernoulli(Tensor self, *, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & bernoulli_out(const at::Tensor & self, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::bernoulli.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & bernoulli_(at::Tensor & self, const at::Tensor & p, ::std::optional<at::Generator> generator); // {"schema": "aten::bernoulli_.Tensor(Tensor(a!) self, Tensor p, *, Generator? generator=None) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & bernoulli_(at::Tensor & self, double p, ::std::optional<at::Generator> generator); // {"schema": "aten::bernoulli_.float(Tensor(a!) self, float p=0.5, *, Generator? generator=None) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor bernoulli(const at::Tensor & self, double p, ::std::optional<at::Generator> generator); // {"schema": "aten::bernoulli.p(Tensor self, float p, *, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor bilinear(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias); // {"schema": "aten::bilinear(Tensor input1, Tensor input2, Tensor weight, Tensor? bias=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor binary_cross_entropy(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction); // {"schema": "aten::binary_cross_entropy(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & binary_cross_entropy_out(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & out); // {"schema": "aten::binary_cross_entropy.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor binary_cross_entropy_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction); // {"schema": "aten::binary_cross_entropy_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & binary_cross_entropy_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & grad_input); // {"schema": "aten::binary_cross_entropy_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor binary_cross_entropy_with_logits(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & pos_weight, int64_t reduction); // {"schema": "aten::binary_cross_entropy_with_logits(Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor bincount(const at::Tensor & self, const ::std::optional<at::Tensor> & weights, c10::SymInt minlength); // {"schema": "aten::bincount(Tensor self, Tensor? weights=None, SymInt minlength=0) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor bitwise_not(const at::Tensor & self); // {"schema": "aten::bitwise_not(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_not_(at::Tensor & self); // {"schema": "aten::bitwise_not_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_not_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & copysign_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::copysign.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor copysign(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::copysign.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & copysign_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::copysign_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor copysign(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::copysign.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & copysign_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::copysign_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & copysign_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::copysign.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor _lazy_clone(const at::Tensor & self); // {"schema": "aten::_lazy_clone(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor logical_not(const at::Tensor & self); // {"schema": "aten::logical_not(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & logical_not_(at::Tensor & self); // {"schema": "aten::logical_not_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & logical_not_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::logical_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor logical_xor(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::logical_xor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & logical_xor_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::logical_xor_(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & logical_xor_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::logical_xor.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor logical_and(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::logical_and(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & logical_and_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::logical_and_(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & logical_and_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::logical_and.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor logical_or(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::logical_or(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & logical_or_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::logical_or_(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & logical_or_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::logical_or.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor blackman_window(int64_t window_length, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::blackman_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor blackman_window(int64_t window_length, bool periodic, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::blackman_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor bmm(const at::Tensor & self, const at::Tensor & mat2); // {"schema": "aten::bmm(Tensor self, Tensor mat2) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & bmm_out(const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out); // {"schema": "aten::bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor bmm(const at::Tensor & self, const at::Tensor & mat2, at::ScalarType out_dtype); // {"schema": "aten::bmm.dtype(Tensor self, Tensor mat2, ScalarType out_dtype) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & bmm_out(const at::Tensor & self, const at::Tensor & mat2, at::ScalarType out_dtype, at::Tensor & out); // {"schema": "aten::bmm.dtype_out(Tensor self, Tensor mat2, ScalarType out_dtype, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
::std::vector<at::Tensor> broadcast_tensors(at::TensorList tensors); // {"schema": "aten::broadcast_tensors(Tensor[] tensors) -> Tensor[]", "dispatch": "False", "default": "True"}
at::Tensor broadcast_to(const at::Tensor & self, c10::SymIntArrayRef size); // {"schema": "aten::broadcast_to(Tensor(a) self, SymInt[] size) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor _sparse_broadcast_to(const at::Tensor & self, at::IntArrayRef size); // {"schema": "aten::_sparse_broadcast_to(Tensor(a) self, int[] size) -> Tensor(a)", "dispatch": "True", "default": "False"}
at::Tensor cat(const at::ITensorListRef & tensors, int64_t dim); // {"schema": "aten::cat(Tensor[] tensors, int dim=0) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & cat_out(const at::ITensorListRef & tensors, int64_t dim, at::Tensor & out); // {"schema": "aten::cat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor concat(at::TensorList tensors, int64_t dim); // {"schema": "aten::concat(Tensor[] tensors, int dim=0) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & concat_out(at::TensorList tensors, int64_t dim, at::Tensor & out); // {"schema": "aten::concat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor concatenate(at::TensorList tensors, int64_t dim); // {"schema": "aten::concatenate(Tensor[] tensors, int dim=0) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & concatenate_out(at::TensorList tensors, int64_t dim, at::Tensor & out); // {"schema": "aten::concatenate.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor block_diag(at::TensorList tensors); // {"schema": "aten::block_diag(Tensor[] tensors) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor ceil(const at::Tensor & self); // {"schema": "aten::ceil(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & ceil_(at::Tensor & self); // {"schema": "aten::ceil_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & ceil_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor chain_matmul(at::TensorList matrices); // {"schema": "aten::chain_matmul(Tensor[] matrices) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & chain_matmul_out(at::TensorList matrices, at::Tensor & out); // {"schema": "aten::chain_matmul.out(Tensor[] matrices, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> unsafe_chunk(const at::Tensor & self, int64_t chunks, int64_t dim); // {"schema": "aten::unsafe_chunk(Tensor self, int chunks, int dim=0) -> Tensor[]", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> chunk(const at::Tensor & self, int64_t chunks, int64_t dim); // {"schema": "aten::chunk(Tensor(a -> *) self, int chunks, int dim=0) -> Tensor(a)[]", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> tensor_split(const at::Tensor & self, c10::SymInt sections, int64_t dim); // {"schema": "aten::tensor_split.sections(Tensor(a -> *) self, SymInt sections, int dim=0) -> Tensor(a)[]", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> tensor_split(const at::Tensor & self, c10::SymIntArrayRef indices, int64_t dim); // {"schema": "aten::tensor_split.indices(Tensor(a -> *) self, SymInt[] indices, int dim=0) -> Tensor(a)[]", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> tensor_split(const at::Tensor & self, const at::Tensor & tensor_indices_or_sections, int64_t dim); // {"schema": "aten::tensor_split.tensor_indices_or_sections(Tensor(a -> *) self, Tensor tensor_indices_or_sections, int dim=0) -> Tensor(a)[]", "dispatch": "False", "default": "True"}
at::Tensor clamp(const at::Tensor & self, const ::std::optional<at::Scalar> & min, const ::std::optional<at::Scalar> & max); // {"schema": "aten::clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor clamp(const at::Tensor & self, const ::std::optional<at::Tensor> & min, const ::std::optional<at::Tensor> & max); // {"schema": "aten::clamp.Tensor(Tensor self, Tensor? min=None, Tensor? max=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & clamp_(at::Tensor & self, const ::std::optional<at::Scalar> & min, const ::std::optional<at::Scalar> & max); // {"schema": "aten::clamp_(Tensor(a!) self, Scalar? min=None, Scalar? max=None) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & clamp_(at::Tensor & self, const ::std::optional<at::Tensor> & min, const ::std::optional<at::Tensor> & max); // {"schema": "aten::clamp_.Tensor(Tensor(a!) self, Tensor? min=None, Tensor? max=None) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & clamp_out(const at::Tensor & self, const ::std::optional<at::Scalar> & min, const ::std::optional<at::Scalar> & max, at::Tensor & out); // {"schema": "aten::clamp.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & clamp_out(const at::Tensor & self, const ::std::optional<at::Tensor> & min, const ::std::optional<at::Tensor> & max, at::Tensor & out); // {"schema": "aten::clamp.Tensor_out(Tensor self, Tensor? min=None, Tensor? max=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor clamp_max(const at::Tensor & self, const at::Scalar & max); // {"schema": "aten::clamp_max(Tensor self, Scalar max) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor clamp_max(const at::Tensor & self, const at::Tensor & max); // {"schema": "aten::clamp_max.Tensor(Tensor self, Tensor max) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & clamp_max_(at::Tensor & self, const at::Scalar & max); // {"schema": "aten::clamp_max_(Tensor(a!) self, Scalar max) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & clamp_max_(at::Tensor & self, const at::Tensor & max); // {"schema": "aten::clamp_max_.Tensor(Tensor(a!) self, Tensor max) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & clamp_max_out(const at::Tensor & self, const at::Scalar & max, at::Tensor & out); // {"schema": "aten::clamp_max.out(Tensor self, Scalar max, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & clamp_max_out(const at::Tensor & self, const at::Tensor & max, at::Tensor & out); // {"schema": "aten::clamp_max.Tensor_out(Tensor self, Tensor max, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor clamp_min(const at::Tensor & self, const at::Scalar & min); // {"schema": "aten::clamp_min(Tensor self, Scalar min) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor clamp_min(const at::Tensor & self, const at::Tensor & min); // {"schema": "aten::clamp_min.Tensor(Tensor self, Tensor min) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & clamp_min_(at::Tensor & self, const at::Scalar & min); // {"schema": "aten::clamp_min_(Tensor(a!) self, Scalar min) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & clamp_min_(at::Tensor & self, const at::Tensor & min); // {"schema": "aten::clamp_min_.Tensor(Tensor(a!) self, Tensor min) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & clamp_min_out(const at::Tensor & self, const at::Scalar & min, at::Tensor & out); // {"schema": "aten::clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & clamp_min_out(const at::Tensor & self, const at::Tensor & min, at::Tensor & out); // {"schema": "aten::clamp_min.Tensor_out(Tensor self, Tensor min, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor clip(const at::Tensor & self, const ::std::optional<at::Scalar> & min, const ::std::optional<at::Scalar> & max); // {"schema": "aten::clip(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor clip(const at::Tensor & self, const ::std::optional<at::Tensor> & min, const ::std::optional<at::Tensor> & max); // {"schema": "aten::clip.Tensor(Tensor self, Tensor? min=None, Tensor? max=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & clip_(at::Tensor & self, const ::std::optional<at::Scalar> & min, const ::std::optional<at::Scalar> & max); // {"schema": "aten::clip_(Tensor(a!) self, Scalar? min=None, Scalar? max=None) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & clip_(at::Tensor & self, const ::std::optional<at::Tensor> & min, const ::std::optional<at::Tensor> & max); // {"schema": "aten::clip_.Tensor(Tensor(a!) self, Tensor? min=None, Tensor? max=None) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & clip_out(const at::Tensor & self, const ::std::optional<at::Scalar> & min, const ::std::optional<at::Scalar> & max, at::Tensor & out); // {"schema": "aten::clip.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & clip_out(const at::Tensor & self, const ::std::optional<at::Tensor> & min, const ::std::optional<at::Tensor> & max, at::Tensor & out); // {"schema": "aten::clip.Tensor_out(Tensor self, Tensor? min=None, Tensor? max=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
bool cudnn_is_acceptable(const at::Tensor & self); // {"schema": "aten::cudnn_is_acceptable(Tensor self) -> bool", "dispatch": "False", "default": "True"}
at::Tensor complex(const at::Tensor & real, const at::Tensor & imag); // {"schema": "aten::complex(Tensor real, Tensor imag) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & complex_out(const at::Tensor & real, const at::Tensor & imag, at::Tensor & out); // {"schema": "aten::complex.out(Tensor real, Tensor imag, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor polar(const at::Tensor & abs, const at::Tensor & angle); // {"schema": "aten::polar(Tensor abs, Tensor angle) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & polar_out(const at::Tensor & abs, const at::Tensor & angle, at::Tensor & out); // {"schema": "aten::polar.out(Tensor abs, Tensor angle, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor constant_pad_nd(const at::Tensor & self, c10::SymIntArrayRef pad, const at::Scalar & value); // {"schema": "aten::constant_pad_nd(Tensor self, SymInt[] pad, Scalar value=0) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor contiguous(const at::Tensor & self, at::MemoryFormat memory_format); // {"schema": "aten::contiguous(Tensor(a) self, *, MemoryFormat memory_format=contiguous_format) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor convolution(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups); // {"schema": "aten::convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups) -> Tensor", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> convolution_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalSymIntArrayRef bias_sizes, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask); // {"schema": "aten::convolution_backward(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "True"}
at::Tensor convolution_overrideable(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups); // {"schema": "aten::convolution_overrideable(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups) -> Tensor", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> convolution_backward_overrideable(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask); // {"schema": "aten::convolution_backward_overrideable(Tensor grad_output, Tensor input, Tensor weight, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)", "dispatch": "True", "default": "True"}
at::Tensor _convolution(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & 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); // {"schema": "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", "dispatch": "True", "default": "True"}
at::Tensor _convolution(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & 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); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor _convolution_mode(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation, c10::SymInt groups); // {"schema": "aten::_convolution_mode(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, str padding, SymInt[] dilation, SymInt groups) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _convolution_double_backward(const ::std::optional<at::Tensor> & ggI, const ::std::optional<at::Tensor> & ggW, const ::std::optional<at::Tensor> & ggb, const at::Tensor & gO, const at::Tensor & weight, const at::Tensor & self, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask); // {"schema": "aten::_convolution_double_backward(Tensor? ggI, Tensor? ggW, Tensor? ggb, Tensor gO, Tensor weight, Tensor self, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)", "dispatch": "False", "default": "True"}
at::Tensor conv1d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor conv2d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor conv3d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor conv1d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation, c10::SymInt groups); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor conv2d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation, c10::SymInt groups); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor conv3d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation, c10::SymInt groups); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor conv_tbc(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad); // {"schema": "aten::conv_tbc(Tensor self, Tensor weight, Tensor bias, int pad=0) -> Tensor", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> conv_tbc_backward(const at::Tensor & self, const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, int64_t pad); // {"schema": "aten::conv_tbc_backward(Tensor self, Tensor input, Tensor weight, Tensor bias, int pad) -> (Tensor, Tensor, Tensor)", "dispatch": "False", "default": "True"}
at::Tensor conv_transpose1d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymInt groups, c10::SymIntArrayRef dilation); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor conv_transpose2d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymInt groups, c10::SymIntArrayRef dilation); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor conv_transpose3d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymInt groups, c10::SymIntArrayRef dilation); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor copy(const at::Tensor & self, const at::Tensor & src, bool non_blocking); // {"schema": "aten::copy(Tensor self, Tensor src, bool non_blocking=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & copy_(at::Tensor & self, const at::Tensor & src, bool non_blocking); // {"schema": "aten::copy_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor _copy_from(const at::Tensor & self, const at::Tensor & dst, bool non_blocking); // {"schema": "aten::_copy_from(Tensor self, Tensor dst, bool non_blocking=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _copy_from_and_resize(const at::Tensor & self, const at::Tensor & dst); // {"schema": "aten::_copy_from_and_resize(Tensor self, Tensor dst) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor cos(const at::Tensor & self); // {"schema": "aten::cos(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & cos_(at::Tensor & self); // {"schema": "aten::cos_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & cos_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::cos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor cosh(const at::Tensor & self); // {"schema": "aten::cosh(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & cosh_(at::Tensor & self); // {"schema": "aten::cosh_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & cosh_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor cosine_embedding_loss(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin, int64_t reduction); // {"schema": "aten::cosine_embedding_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor count_nonzero(const at::Tensor & self, at::IntArrayRef dim); // {"schema": "aten::count_nonzero.dim_IntList(Tensor self, int[] dim) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor count_nonzero(const at::Tensor & self, ::std::optional<int64_t> dim); // {"schema": "aten::count_nonzero(Tensor self, int? dim=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor cov(const at::Tensor & self, int64_t correction, const ::std::optional<at::Tensor> & fweights, const ::std::optional<at::Tensor> & aweights); // {"schema": "aten::cov(Tensor self, *, int correction=1, Tensor? fweights=None, Tensor? aweights=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor corrcoef(const at::Tensor & self); // {"schema": "aten::corrcoef(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor cudnn_affine_grid_generator(const at::Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W); // {"schema": "aten::cudnn_affine_grid_generator(Tensor theta, int N, int C, int H, int W) -> Tensor grid", "dispatch": "True", "default": "False"}
at::Tensor cudnn_affine_grid_generator_backward(const at::Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W); // {"schema": "aten::cudnn_affine_grid_generator_backward(Tensor grad, int N, int C, int H, int W) -> Tensor grad_theta", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> cudnn_batch_norm(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double exponential_average_factor, double epsilon); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> cudnn_batch_norm_out(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double exponential_average_factor, double epsilon, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3); // {"schema": "aten::cudnn_batch_norm.out(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> cudnn_batch_norm_backward(const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_var, double epsilon, const at::Tensor & reserveSpace); // {"schema": "aten::cudnn_batch_norm_backward(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon, Tensor reserveSpace) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor 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); // {"schema": "aten::cudnn_convolution(Tensor self, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & cudnn_convolution_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 & out); // {"schema": "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!)", "dispatch": "True", "default": "False"}
at::Tensor 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); // {"schema": "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", "dispatch": "True", "default": "False"}
at::Tensor _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); // {"schema": "aten::_mps_convolution_transpose(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> mps_convolution_transpose_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,2> output_mask); // {"schema": "aten::mps_convolution_transpose_backward(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[2] output_mask) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor cudnn_convolution_relu(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups); // {"schema": "aten::cudnn_convolution_relu(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor cudnn_convolution_add_relu(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups); // {"schema": "aten::cudnn_convolution_add_relu(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor cudnn_grid_sampler(const at::Tensor & self, const at::Tensor & grid); // {"schema": "aten::cudnn_grid_sampler(Tensor self, Tensor grid) -> Tensor output", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> cudnn_grid_sampler_backward(const at::Tensor & self, const at::Tensor & grid, const at::Tensor & grad_output); // {"schema": "aten::cudnn_grid_sampler_backward(Tensor self, Tensor grid, Tensor grad_output) -> (Tensor grad_self, Tensor grad_grid)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> cummax(const at::Tensor & self, int64_t dim); // {"schema": "aten::cummax(Tensor self, int dim) -> (Tensor values, Tensor indices)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> cummax_out(const at::Tensor & self, int64_t dim, at::Tensor & values, at::Tensor & indices); // {"schema": "aten::cummax.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)", "dispatch": "True", "default": "True"}
void _cummax_helper(const at::Tensor & self, at::Tensor & values, at::Tensor & indices, int64_t dim); // {"schema": "aten::_cummax_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> ()", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> cummin(const at::Tensor & self, int64_t dim); // {"schema": "aten::cummin(Tensor self, int dim) -> (Tensor values, Tensor indices)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> cummin_out(const at::Tensor & self, int64_t dim, at::Tensor & values, at::Tensor & indices); // {"schema": "aten::cummin.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)", "dispatch": "True", "default": "True"}
void _cummin_helper(const at::Tensor & self, at::Tensor & values, at::Tensor & indices, int64_t dim); // {"schema": "aten::_cummin_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> ()", "dispatch": "True", "default": "False"}
at::Tensor cummaxmin_backward(const at::Tensor & grad, const at::Tensor & input, const at::Tensor & indices, int64_t dim); // {"schema": "aten::cummaxmin_backward(Tensor grad, Tensor input, Tensor indices, int dim) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor cumprod(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::cumprod(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & cumprod_(at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::cumprod_(Tensor(a!) self, int dim, *, ScalarType? dtype=None) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & cumprod_out(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor cumprod_backward(const at::Tensor & grad, const at::Tensor & input, int64_t dim, const at::Tensor & output); // {"schema": "aten::cumprod_backward(Tensor grad, Tensor input, int dim, Tensor output) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor cumsum(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::cumsum(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & cumsum_(at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::cumsum_(Tensor(a!) self, int dim, *, ScalarType? dtype=None) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & cumsum_out(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::cumsum.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor cumulative_trapezoid(const at::Tensor & y, const at::Tensor & x, int64_t dim); // {"schema": "aten::cumulative_trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor cumulative_trapezoid(const at::Tensor & y, const at::Scalar & dx, int64_t dim); // {"schema": "aten::cumulative_trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor 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); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, int64_t reduction, bool zero_infinity); // {"schema": "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", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> _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); // {"schema": "aten::_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, bool zero_infinity); // {"schema": "aten::_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor _ctc_loss_backward(const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity); // {"schema": "aten::_ctc_loss_backward(Tensor grad, Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, Tensor neg_log_likelihood, Tensor log_alpha, int blank, bool zero_infinity=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _ctc_loss_backward(const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity); // {"schema": "aten::_ctc_loss_backward.Tensor(Tensor grad, Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, Tensor neg_log_likelihood, Tensor log_alpha, int blank, bool zero_infinity=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor diag_embed(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2); // {"schema": "aten::diag_embed(Tensor self, int offset=0, int dim1=-2, int dim2=-1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor diagflat(const at::Tensor & self, int64_t offset); // {"schema": "aten::diagflat(Tensor self, int offset=0) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor diagonal(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2); // {"schema": "aten::diagonal(Tensor(a) self, int offset=0, int dim1=0, int dim2=1) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor linalg_diagonal(const at::Tensor & A, int64_t offset, int64_t dim1, int64_t dim2); // {"schema": "aten::linalg_diagonal(Tensor(a) A, *, int offset=0, int dim1=-2, int dim2=-1) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor diagonal_backward(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2); // {"schema": "aten::diagonal_backward(Tensor grad_output, SymInt[] input_sizes, int offset, int dim1, int dim2) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & fill_diagonal_(at::Tensor & self, const at::Scalar & fill_value, bool wrap); // {"schema": "aten::fill_diagonal_(Tensor(a!) self, Scalar fill_value, bool wrap=False) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor diff(const at::Tensor & self, int64_t n, int64_t dim, const ::std::optional<at::Tensor> & prepend, const ::std::optional<at::Tensor> & append); // {"schema": "aten::diff(Tensor self, int n=1, int dim=-1, Tensor? prepend=None, Tensor? append=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & diff_out(const at::Tensor & self, int64_t n, int64_t dim, const ::std::optional<at::Tensor> & prepend, const ::std::optional<at::Tensor> & append, at::Tensor & out); // {"schema": "aten::diff.out(Tensor self, int n=1, int dim=-1, Tensor? prepend=None, Tensor? append=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> gradient(const at::Tensor & self, const ::std::optional<at::Scalar> & spacing, ::std::optional<int64_t> dim, int64_t edge_order); // {"schema": "aten::gradient.scalarint(Tensor self, *, Scalar? spacing=None, int? dim=None, int edge_order=1) -> Tensor[]", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> gradient(const at::Tensor & self, const at::Scalar & spacing, at::IntArrayRef dim, int64_t edge_order); // {"schema": "aten::gradient.scalararray(Tensor self, *, Scalar spacing, int[] dim, int edge_order=1) -> Tensor[]", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> gradient(const at::Tensor & self, at::IntArrayRef dim, int64_t edge_order); // {"schema": "aten::gradient.array(Tensor self, *, int[] dim, int edge_order=1) -> Tensor[]", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> gradient(const at::Tensor & self, at::ArrayRef<at::Scalar> spacing, ::std::optional<int64_t> dim, int64_t edge_order); // {"schema": "aten::gradient.scalarrayint(Tensor self, *, Scalar[] spacing, int? dim=None, int edge_order=1) -> Tensor[]", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> gradient(const at::Tensor & self, at::ArrayRef<at::Scalar> spacing, at::IntArrayRef dim, int64_t edge_order); // {"schema": "aten::gradient.scalarrayarray(Tensor self, *, Scalar[] spacing, int[] dim, int edge_order=1) -> Tensor[]", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> gradient(const at::Tensor & self, at::TensorList spacing, ::std::optional<int64_t> dim, int64_t edge_order); // {"schema": "aten::gradient.tensorarrayint(Tensor self, *, Tensor[] spacing, int? dim=None, int edge_order=1) -> Tensor[]", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> gradient(const at::Tensor & self, at::TensorList spacing, at::IntArrayRef dim, int64_t edge_order); // {"schema": "aten::gradient.tensorarray(Tensor self, *, Tensor[] spacing, int[] dim, int edge_order=1) -> Tensor[]", "dispatch": "False", "default": "True"}
at::Tensor div(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::div.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & div_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::div_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & div_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor div(const at::Tensor & self, const at::Tensor & other, ::std::optional<c10::string_view> rounding_mode); // {"schema": "aten::div.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & div_(at::Tensor & self, const at::Tensor & other, ::std::optional<c10::string_view> rounding_mode); // {"schema": "aten::div_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & div_out(const at::Tensor & self, const at::Tensor & other, ::std::optional<c10::string_view> rounding_mode, at::Tensor & out); // {"schema": "aten::div.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor div(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::div.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & div_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::div_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor div(const at::Tensor & self, const at::Scalar & other, ::std::optional<c10::string_view> rounding_mode); // {"schema": "aten::div.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & div_(at::Tensor & self, const at::Scalar & other, ::std::optional<c10::string_view> rounding_mode); // {"schema": "aten::div_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor divide(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::divide.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & divide_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & divide_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor divide(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::divide.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & divide_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor divide(const at::Tensor & self, const at::Tensor & other, ::std::optional<c10::string_view> rounding_mode); // {"schema": "aten::divide.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & divide_(at::Tensor & self, const at::Tensor & other, ::std::optional<c10::string_view> rounding_mode); // {"schema": "aten::divide_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & divide_out(const at::Tensor & self, const at::Tensor & other, ::std::optional<c10::string_view> rounding_mode, at::Tensor & out); // {"schema": "aten::divide.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor divide(const at::Tensor & self, const at::Scalar & other, ::std::optional<c10::string_view> rounding_mode); // {"schema": "aten::divide.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & divide_(at::Tensor & self, const at::Scalar & other, ::std::optional<c10::string_view> rounding_mode); // {"schema": "aten::divide_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor true_divide(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::true_divide.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & true_divide_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::true_divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & true_divide_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::true_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor true_divide(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::true_divide.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & true_divide_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::true_divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor dot(const at::Tensor & self, const at::Tensor & tensor); // {"schema": "aten::dot(Tensor self, Tensor tensor) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & dot_out(const at::Tensor & self, const at::Tensor & tensor, at::Tensor & out); // {"schema": "aten::dot.out(Tensor self, Tensor tensor, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor vdot(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::vdot(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & vdot_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::vdot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor einsum(c10::string_view equation, at::TensorList tensors, at::OptionalIntArrayRef path); // {"schema": "aten::einsum(str equation, Tensor[] tensors, *, int[]? path=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor embedding(const at::Tensor & weight, const at::Tensor & indices, c10::SymInt padding_idx, bool scale_grad_by_freq, bool sparse); // {"schema": "aten::embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor embedding_backward(const at::Tensor & grad, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq, bool sparse); // {"schema": "aten::embedding_backward(Tensor grad, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, bool sparse) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor embedding_dense_backward(const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq); // {"schema": "aten::embedding_dense_backward(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & embedding_renorm_(at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type); // {"schema": "aten::embedding_renorm_(Tensor(a!) self, Tensor indices, float max_norm, float norm_type) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor embedding_sparse_backward(const at::Tensor & grad, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq); // {"schema": "aten::embedding_sparse_backward(Tensor grad, Tensor indices, int num_weights, int padding_idx, bool scale_grad_by_freq) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _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<at::Tensor> & per_sample_weights, bool include_last_offset, int64_t padding_idx); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _rowwise_prune(const at::Tensor & weight, const at::Tensor & mask, at::ScalarType compressed_indices_dtype); // {"schema": "aten::_rowwise_prune(Tensor weight, Tensor mask, ScalarType compressed_indices_dtype) -> (Tensor, Tensor)", "dispatch": "False", "default": "True"}
at::Tensor row_stack(at::TensorList tensors); // {"schema": "aten::row_stack(Tensor[] tensors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & row_stack_out(at::TensorList tensors, at::Tensor & out); // {"schema": "aten::row_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> embedding_bag(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const ::std::optional<at::Tensor> & per_sample_weights, bool include_last_offset); // {"schema": "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)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> embedding_bag(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const ::std::optional<at::Tensor> & per_sample_weights, bool include_last_offset, ::std::optional<int64_t> padding_idx); // {"schema": "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)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _embedding_bag(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const ::std::optional<at::Tensor> & per_sample_weights, bool include_last_offset, int64_t padding_idx); // {"schema": "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)", "dispatch": "True", "default": "False"}
at::Tensor _embedding_bag_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx); // {"schema": "aten::_embedding_bag_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _embedding_bag_sparse_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx); // {"schema": "aten::_embedding_bag_sparse_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _embedding_bag_dense_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx); // {"schema": "aten::_embedding_bag_dense_backward(Tensor grad, Tensor indices, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _embedding_bag_per_sample_weights_backward(const at::Tensor & grad, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, int64_t mode, int64_t padding_idx); // {"schema": "aten::_embedding_bag_per_sample_weights_backward(Tensor grad, Tensor weight, Tensor indices, Tensor offsets, Tensor offset2bag, int mode, int padding_idx=-1) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor empty(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::empty.memory_format(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor empty_permuted(c10::SymIntArrayRef size, at::IntArrayRef physical_layout, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::empty_permuted(SymInt[] size, int[] physical_layout, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor new_empty(const at::Tensor & self, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::new_empty(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor new_empty_strided(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::new_empty_strided(Tensor self, SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor new_full(const at::Tensor & self, c10::SymIntArrayRef size, const at::Scalar & fill_value, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::new_full(Tensor self, SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor new_zeros(const at::Tensor & self, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::new_zeros(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor new_ones(const at::Tensor & self, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::new_ones(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _empty_affine_quantized(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, double scale, int64_t zero_point, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "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", "dispatch": "True", "default": "False"}
at::Tensor _empty_per_channel_affine_quantized(c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "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", "dispatch": "True", "default": "False"}
const at::Tensor & resize_(const at::Tensor & self, c10::SymIntArrayRef size, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::resize_(Tensor(a!) self, SymInt[] size, *, MemoryFormat? memory_format=None) -> Tensor(a!)", "dispatch": "True", "default": "False"}
const at::Tensor & _resize_output_(const at::Tensor & self, c10::SymIntArrayRef size, at::Device device); // {"schema": "aten::_resize_output_(Tensor(a!) self, SymInt[] size, Device device) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor empty_quantized(at::IntArrayRef size, const at::Tensor & qtensor, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "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", "dispatch": "True", "default": "False"}
at::Tensor & empty_out(c10::SymIntArrayRef size, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::empty.out(SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor empty_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::empty_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor empty_strided(c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::empty_strided(SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor erf(const at::Tensor & self); // {"schema": "aten::erf(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & erf_(at::Tensor & self); // {"schema": "aten::erf_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & erf_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor erfc(const at::Tensor & self); // {"schema": "aten::erfc(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & erfc_(at::Tensor & self); // {"schema": "aten::erfc_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & erfc_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor exp(const at::Tensor & self); // {"schema": "aten::exp(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & exp_(at::Tensor & self); // {"schema": "aten::exp_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & exp_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor exp2(const at::Tensor & self); // {"schema": "aten::exp2(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & exp2_(at::Tensor & self); // {"schema": "aten::exp2_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & exp2_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor expm1(const at::Tensor & self); // {"schema": "aten::expm1(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & expm1_(at::Tensor & self); // {"schema": "aten::expm1_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & expm1_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor expand(const at::Tensor & self, c10::SymIntArrayRef size, bool implicit); // {"schema": "aten::expand(Tensor(a) self, SymInt[] size, *, bool implicit=False) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor expand_as(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::expand_as(Tensor(a) self, Tensor other) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor eye(c10::SymInt n, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::eye(SymInt n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor eye(c10::SymInt n, c10::SymInt m, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::eye.m(SymInt n, SymInt m, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & eye_out(c10::SymInt n, at::Tensor & out); // {"schema": "aten::eye.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & eye_out(c10::SymInt n, c10::SymInt m, at::Tensor & out); // {"schema": "aten::eye.m_out(SymInt n, SymInt m, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor flatten(const at::Tensor & self, int64_t start_dim, int64_t end_dim); // {"schema": "aten::flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor unflatten(const at::Tensor & self, int64_t dim, c10::SymIntArrayRef sizes); // {"schema": "aten::unflatten.int(Tensor(a) self, int dim, SymInt[] sizes) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor fill(const at::Tensor & self, const at::Scalar & value); // {"schema": "aten::fill.Scalar(Tensor self, Scalar value) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor fill(const at::Tensor & self, const at::Tensor & value); // {"schema": "aten::fill.Tensor(Tensor self, Tensor value) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & fill_(at::Tensor & self, const at::Scalar & value); // {"schema": "aten::fill_.Scalar(Tensor(a!) self, Scalar value) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & fill_(at::Tensor & self, const at::Tensor & value); // {"schema": "aten::fill_.Tensor(Tensor(a!) self, Tensor value) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor floor(const at::Tensor & self); // {"schema": "aten::floor(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & floor_(at::Tensor & self); // {"schema": "aten::floor_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & floor_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor floor_divide(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::floor_divide(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & floor_divide_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::floor_divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & floor_divide_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::floor_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor floor_divide(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::floor_divide.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & floor_divide_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::floor_divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor frac(const at::Tensor & self); // {"schema": "aten::frac(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & frac_(at::Tensor & self); // {"schema": "aten::frac_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & frac_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::frac.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor full(c10::SymIntArrayRef size, const at::Scalar & fill_value, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & full_out(c10::SymIntArrayRef size, const at::Scalar & fill_value, at::Tensor & out); // {"schema": "aten::full.out(SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor full_like(const at::Tensor & self, const at::Scalar & fill_value, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "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", "dispatch": "True", "default": "True"}
at::Tensor from_file(c10::string_view filename, ::std::optional<bool> shared, ::std::optional<int64_t> size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "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", "dispatch": "True", "default": "False"}
at::Tensor & gcd_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::gcd.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor gcd(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::gcd(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & gcd_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::gcd_(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & lcm_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::lcm.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor lcm(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::lcm(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & lcm_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::lcm_(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor grid_sampler(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); // {"schema": "aten::grid_sampler(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor grid_sampler_2d(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); // {"schema": "aten::grid_sampler_2d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> grid_sampler_2d_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask); // {"schema": "aten::grid_sampler_2d_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor _grid_sampler_2d_cpu_fallback(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); // {"schema": "aten::_grid_sampler_2d_cpu_fallback(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> _grid_sampler_2d_cpu_fallback_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); // {"schema": "aten::_grid_sampler_2d_cpu_fallback_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> (Tensor, Tensor)", "dispatch": "True", "default": "True"}
at::Tensor grid_sampler_3d(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); // {"schema": "aten::grid_sampler_3d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> grid_sampler_3d_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask); // {"schema": "aten::grid_sampler_3d_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor hann_window(int64_t window_length, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::hann_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor hann_window(int64_t window_length, bool periodic, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::hann_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor hamming_window(int64_t window_length, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::hamming_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor hamming_window(int64_t window_length, bool periodic, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::hamming_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor hamming_window(int64_t window_length, bool periodic, double alpha, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "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", "dispatch": "True", "default": "True"}
at::Tensor hamming_window(int64_t window_length, bool periodic, double alpha, double beta, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "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", "dispatch": "True", "default": "True"}
at::Tensor kaiser_window(int64_t window_length, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::kaiser_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor kaiser_window(int64_t window_length, bool periodic, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::kaiser_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor kaiser_window(int64_t window_length, bool periodic, double beta, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "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", "dispatch": "True", "default": "True"}
at::Tensor hinge_embedding_loss(const at::Tensor & self, const at::Tensor & target, double margin, int64_t reduction); // {"schema": "aten::hinge_embedding_loss(Tensor self, Tensor target, float margin=1.0, int reduction=Mean) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor group_norm(const at::Tensor & input, int64_t num_groups, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, double eps, bool cudnn_enabled); // {"schema": "aten::group_norm(Tensor input, int num_groups, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enabled=True) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps); // {"schema": "aten::native_group_norm(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array<bool,3> output_mask); // {"schema": "aten::native_group_norm_backward(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor _fft_r2c(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided); // {"schema": "aten::_fft_r2c(Tensor self, int[] dim, int normalization, bool onesided) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & _fft_r2c_out(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided, at::Tensor & out); // {"schema": "aten::_fft_r2c.out(Tensor self, int[] dim, int normalization, bool onesided, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _fft_c2r(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, c10::SymInt last_dim_size); // {"schema": "aten::_fft_c2r(Tensor self, int[] dim, int normalization, SymInt last_dim_size) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & _fft_c2r_out(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, c10::SymInt last_dim_size, at::Tensor & out); // {"schema": "aten::_fft_c2r.out(Tensor self, int[] dim, int normalization, SymInt last_dim_size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _fft_c2c(const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward); // {"schema": "aten::_fft_c2c(Tensor self, SymInt[] dim, int normalization, bool forward) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & _fft_c2c_out(const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward, at::Tensor & out); // {"schema": "aten::_fft_c2c.out(Tensor self, SymInt[] dim, int normalization, bool forward, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
void _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); // {"schema": "aten::_validate_compressed_sparse_indices(bool is_crow, Tensor compressed_idx, Tensor plain_idx, int cdim, int dim, int nnz) -> ()", "dispatch": "True", "default": "False"}
int64_t _cufft_get_plan_cache_size(at::DeviceIndex device_index); // {"schema": "aten::_cufft_get_plan_cache_size(DeviceIndex device_index) -> int", "dispatch": "False", "default": "True"}
int64_t _cufft_get_plan_cache_max_size(at::DeviceIndex device_index); // {"schema": "aten::_cufft_get_plan_cache_max_size(DeviceIndex device_index) -> int", "dispatch": "False", "default": "True"}
void _cufft_set_plan_cache_max_size(at::DeviceIndex device_index, int64_t max_size); // {"schema": "aten::_cufft_set_plan_cache_max_size(DeviceIndex device_index, int max_size) -> ()", "dispatch": "False", "default": "True"}
void _cufft_clear_plan_cache(at::DeviceIndex device_index); // {"schema": "aten::_cufft_clear_plan_cache(DeviceIndex device_index) -> ()", "dispatch": "False", "default": "True"}
at::Tensor index(const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices); // {"schema": "aten::index.Tensor(Tensor self, Tensor?[] indices) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & index_out(const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, at::Tensor & out); // {"schema": "aten::index.Tensor_out(Tensor self, Tensor?[] indices, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _unsafe_index(const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices); // {"schema": "aten::_unsafe_index.Tensor(Tensor self, Tensor?[] indices) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _unsafe_masked_index(const at::Tensor & self, const at::Tensor & mask, const c10::List<::std::optional<at::Tensor>> & indices, const at::Scalar & fill); // {"schema": "aten::_unsafe_masked_index(Tensor self, Tensor mask, Tensor?[] indices, Scalar fill) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _unsafe_masked_index_put_accumulate(const at::Tensor & self, const at::Tensor & mask, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values); // {"schema": "aten::_unsafe_masked_index_put_accumulate(Tensor self, Tensor mask, Tensor?[] indices, Tensor values) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & index_copy_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, at::Tensor & out); // {"schema": "aten::index_copy.out(Tensor self, int dim, Tensor index, Tensor source, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & index_copy_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source); // {"schema": "aten::index_copy_(Tensor(a!) self, int dim, Tensor index, Tensor source) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor index_copy(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source); // {"schema": "aten::index_copy(Tensor self, int dim, Tensor index, Tensor source) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & index_put_(at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate); // {"schema": "aten::index_put_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor index_put(const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate); // {"schema": "aten::index_put(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _unsafe_index_put(const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate); // {"schema": "aten::_unsafe_index_put(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _index_put_impl_(at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate, bool unsafe); // {"schema": "aten::_index_put_impl_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False, bool unsafe=False) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor instance_norm(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool use_input_stats, double momentum, double eps, bool cudnn_enabled); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor isclose(const at::Tensor & self, const at::Tensor & other, double rtol, double atol, bool equal_nan); // {"schema": "aten::isclose(Tensor self, Tensor other, float rtol=1e-05, float atol=1e-08, bool equal_nan=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & isin_out(const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique, bool invert, at::Tensor & out); // {"schema": "aten::isin.Tensor_Tensor_out(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor isin(const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique, bool invert); // {"schema": "aten::isin.Tensor_Tensor(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & isin_out(const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique, bool invert, at::Tensor & out); // {"schema": "aten::isin.Tensor_Scalar_out(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor isin(const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique, bool invert); // {"schema": "aten::isin.Tensor_Scalar(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & isin_out(const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique, bool invert, at::Tensor & out); // {"schema": "aten::isin.Scalar_Tensor_out(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor isin(const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique, bool invert); // {"schema": "aten::isin.Scalar_Tensor(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor isnan(const at::Tensor & self); // {"schema": "aten::isnan(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
bool is_distributed(const at::Tensor & self); // {"schema": "aten::is_distributed(Tensor self) -> bool", "dispatch": "False", "default": "True"}
bool is_floating_point(const at::Tensor & self); // {"schema": "aten::is_floating_point(Tensor self) -> bool", "dispatch": "False", "default": "True"}
bool is_complex(const at::Tensor & self); // {"schema": "aten::is_complex(Tensor self) -> bool", "dispatch": "False", "default": "True"}
bool is_conj(const at::Tensor & self); // {"schema": "aten::is_conj(Tensor self) -> bool", "dispatch": "False", "default": "True"}
bool _is_zerotensor(const at::Tensor & self); // {"schema": "aten::_is_zerotensor(Tensor self) -> bool", "dispatch": "False", "default": "True"}
bool is_neg(const at::Tensor & self); // {"schema": "aten::is_neg(Tensor self) -> bool", "dispatch": "False", "default": "True"}
at::Tensor isreal(const at::Tensor & self); // {"schema": "aten::isreal(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
bool is_nonzero(const at::Tensor & self); // {"schema": "aten::is_nonzero(Tensor self) -> bool", "dispatch": "False", "default": "True"}
bool is_same_size(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::is_same_size(Tensor self, Tensor other) -> bool", "dispatch": "True", "default": "True"}
bool is_signed(const at::Tensor & self); // {"schema": "aten::is_signed(Tensor self) -> bool", "dispatch": "False", "default": "True"}
bool is_inference(const at::Tensor & self); // {"schema": "aten::is_inference(Tensor self) -> bool", "dispatch": "False", "default": "True"}
int64_t numel(const at::Tensor & self); // {"schema": "aten::numel(Tensor self) -> int", "dispatch": "False", "default": "True"}
int64_t dim(const at::Tensor & self); // {"schema": "aten::dim(Tensor self) -> int", "dispatch": "False", "default": "True"}
int64_t get_device(const at::Tensor & self); // {"schema": "aten::get_device(Tensor self) -> int", "dispatch": "False", "default": "True"}
int64_t storage_offset(const at::Tensor & self); // {"schema": "aten::storage_offset(Tensor self) -> int", "dispatch": "False", "default": "True"}
bool is_contiguous(const at::Tensor & self); // {"schema": "aten::is_contiguous(Tensor self) -> bool", "dispatch": "False", "default": "True"}
bool is_contiguous(const at::Tensor & self, at::MemoryFormat memory_format); // {"schema": "aten::is_contiguous.memory_format(Tensor self, MemoryFormat memory_format) -> bool", "dispatch": "False", "default": "True"}
at::Tensor kl_div(const at::Tensor & self, const at::Tensor & target, int64_t reduction, bool log_target); // {"schema": "aten::kl_div(Tensor self, Tensor target, int reduction=Mean, *, bool log_target=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor kron(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::kron(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & kron_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::kron.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> kthvalue(const at::Tensor & self, c10::SymInt k, int64_t dim, bool keepdim); // {"schema": "aten::kthvalue(Tensor self, SymInt k, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> kthvalue_out(const at::Tensor & self, c10::SymInt k, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices); // {"schema": "aten::kthvalue.values(Tensor self, SymInt k, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)", "dispatch": "True", "default": "False"}
at::Tensor layer_norm(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, double eps, bool cudnn_enable); // {"schema": "aten::layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enable=True) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_layer_norm(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, double eps); // {"schema": "aten::native_layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_layer_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask); // {"schema": "aten::native_layer_norm_backward(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor rms_norm(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight, ::std::optional<double> eps); // {"schema": "aten::rms_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight=None, float? eps=None) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> _fused_rms_norm(const at::Tensor & input, at::IntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight, ::std::optional<double> eps); // {"schema": "aten::_fused_rms_norm(Tensor input, int[] normalized_shape, Tensor? weight, float? eps) -> (Tensor, Tensor)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> _fused_rms_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, at::IntArrayRef normalized_shape, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, ::std::array<bool,2> output_mask); // {"schema": "aten::_fused_rms_norm_backward(Tensor grad_out, Tensor input, int[] normalized_shape, Tensor rstd, Tensor? weight, bool[2] output_mask) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor nan_to_num(const at::Tensor & self, ::std::optional<double> nan, ::std::optional<double> posinf, ::std::optional<double> neginf); // {"schema": "aten::nan_to_num(Tensor self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & nan_to_num_(at::Tensor & self, ::std::optional<double> nan, ::std::optional<double> posinf, ::std::optional<double> neginf); // {"schema": "aten::nan_to_num_(Tensor(a!) self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & nan_to_num_out(const at::Tensor & self, ::std::optional<double> nan, ::std::optional<double> posinf, ::std::optional<double> neginf, at::Tensor & out); // {"schema": "aten::nan_to_num.out(Tensor self, float? nan=None, float? posinf=None, float? neginf=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor linear(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias); // {"schema": "aten::linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensor", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> linear_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array<bool,3> output_mask); // {"schema": "aten::linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor & linear_out(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::Tensor & out); // {"schema": "aten::linear.out(Tensor input, Tensor weight, Tensor? bias=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor mkldnn_linear(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias); // {"schema": "aten::mkldnn_linear(Tensor self, Tensor weight, Tensor? bias=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor mkldnn_linear_backward_input(at::IntArrayRef input_size, const at::Tensor & grad_output, const at::Tensor & weight); // {"schema": "aten::mkldnn_linear_backward_input(int[] input_size, Tensor grad_output, Tensor weight) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> mkldnn_linear_backward_weights(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, bool bias_defined); // {"schema": "aten::mkldnn_linear_backward_weights(Tensor grad_output, Tensor input, Tensor weight, bool bias_defined) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> mkldnn_linear_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array<bool,3> output_mask); // {"schema": "aten::mkldnn_linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor _cslt_compress(const at::Tensor & input); // {"schema": "aten::_cslt_compress(Tensor input) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _cslt_sparse_mm(const at::Tensor & compressed_A, const at::Tensor & dense_B, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & alpha, ::std::optional<at::ScalarType> out_dtype, bool transpose_result, int64_t alg_id, int64_t split_k, int64_t split_k_mode); // {"schema": "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, int split_k_mode=-1) -> Tensor", "dispatch": "True", "default": "False"}
int64_t _cslt_sparse_mm_search(const at::Tensor & compressed_A, const at::Tensor & dense_B, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & alpha, ::std::optional<at::ScalarType> out_dtype, bool transpose_result); // {"schema": "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", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _sparse_semi_structured_tile(const at::Tensor & input, c10::string_view algorithm, bool use_cutlass); // {"schema": "aten::_sparse_semi_structured_tile(Tensor input, str algorithm=\"\", bool use_cutlass=True) -> (Tensor, Tensor, Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _sparse_semi_structured_apply(const at::Tensor & input, const at::Tensor & thread_masks); // {"schema": "aten::_sparse_semi_structured_apply(Tensor input, Tensor thread_masks) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor _sparse_semi_structured_apply_dense(const at::Tensor & input, const at::Tensor & thread_masks); // {"schema": "aten::_sparse_semi_structured_apply_dense(Tensor input, Tensor thread_masks) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _sparse_semi_structured_linear(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & meta, const ::std::optional<at::Tensor> & bias, ::std::optional<c10::string_view> activation, ::std::optional<at::ScalarType> out_dtype); // {"schema": "aten::_sparse_semi_structured_linear(Tensor input, Tensor weight, Tensor meta, *, Tensor? bias=None, str? activation=None, ScalarType? out_dtype=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _sparse_semi_structured_mm(const at::Tensor & mat1, const at::Tensor & mat1_meta, const at::Tensor & mat2, ::std::optional<at::ScalarType> out_dtype); // {"schema": "aten::_sparse_semi_structured_mm(Tensor mat1, Tensor mat1_meta, Tensor mat2, *, ScalarType? out_dtype=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _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<at::ScalarType> out_dtype); // {"schema": "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", "dispatch": "True", "default": "False"}
at::Tensor _mixed_dtypes_linear(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & scale, const ::std::optional<at::Tensor> & bias, ::std::optional<c10::string_view> activation); // {"schema": "aten::_mixed_dtypes_linear(Tensor input, Tensor weight, Tensor scale, *, Tensor? bias=None, str? activation=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor 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); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor 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); // {"schema": "aten::fbgemm_linear_int8_weight(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,double,int64_t> fbgemm_linear_quantize_weight(const at::Tensor & input); // {"schema": "aten::fbgemm_linear_quantize_weight(Tensor input) -> (Tensor, Tensor, float, int)", "dispatch": "False", "default": "True"}
at::Tensor fbgemm_pack_gemm_matrix_fp16(const at::Tensor & input); // {"schema": "aten::fbgemm_pack_gemm_matrix_fp16(Tensor input) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _wrapped_linear_prepack(const at::Tensor & weight, const at::Tensor & weight_scale, const at::Tensor & weight_zero_point, const at::Tensor & bias); // {"schema": "aten::_wrapped_linear_prepack(Tensor weight, Tensor weight_scale, Tensor weight_zero_point, Tensor bias) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _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); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor fbgemm_linear_fp16_weight_fp32_activation(const at::Tensor & input, const at::Tensor & packed_weight, const ::std::optional<at::Tensor> & bias); // {"schema": "aten::fbgemm_linear_fp16_weight_fp32_activation(Tensor input, Tensor packed_weight, Tensor? bias) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor fbgemm_linear_fp16_weight_fp32_activation(const at::Tensor & input, const at::Tensor & packed_weight, const ::std::optional<at::Tensor> & bias, at::Tensor & output); // {"schema": "aten::fbgemm_linear_fp16_weight_fp32_activation.out(Tensor input, Tensor packed_weight, Tensor? bias, Tensor(a!) output) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor fbgemm_linear_fp16_weight(const at::Tensor & input, const at::Tensor & packed_weight, const at::Tensor & bias); // {"schema": "aten::fbgemm_linear_fp16_weight(Tensor input, Tensor packed_weight, Tensor bias) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor fbgemm_linear_fp16_weight(const at::Tensor & input, const at::Tensor & packed_weight, const at::Tensor & bias, at::Tensor & output); // {"schema": "aten::fbgemm_linear_fp16_weight.out(Tensor input, Tensor packed_weight, Tensor bias, Tensor(a!) output) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor fbgemm_pack_quantized_matrix(const at::Tensor & input); // {"schema": "aten::fbgemm_pack_quantized_matrix(Tensor input) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor fbgemm_pack_quantized_matrix(const at::Tensor & input, int64_t K, int64_t N); // {"schema": "aten::fbgemm_pack_quantized_matrix.KN(Tensor input, int K, int N) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor ldexp(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::ldexp.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & ldexp_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::ldexp_(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & ldexp_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::ldexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor linspace(const at::Scalar & start, const at::Scalar & end, int64_t steps, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::linspace(Scalar start, Scalar end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor linspace(const at::Tensor & start, const at::Tensor & end, int64_t steps, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::linspace.Tensor_Tensor(Tensor start, Tensor end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor linspace(const at::Tensor & start, const at::Scalar & end, int64_t steps, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::linspace.Tensor_Scalar(Tensor start, Scalar end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor linspace(const at::Scalar & start, const at::Tensor & end, int64_t steps, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::linspace.Scalar_Tensor(Scalar start, Tensor end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & linspace_out(const at::Scalar & start, const at::Scalar & end, int64_t steps, at::Tensor & out); // {"schema": "aten::linspace.out(Scalar start, Scalar end, int steps, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & linspace_out(const at::Tensor & start, const at::Tensor & end, int64_t steps, at::Tensor & out); // {"schema": "aten::linspace.Tensor_Tensor_out(Tensor start, Tensor end, int steps, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & linspace_out(const at::Tensor & start, const at::Scalar & end, int64_t steps, at::Tensor & out); // {"schema": "aten::linspace.Tensor_Scalar_out(Tensor start, Scalar end, int steps, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & linspace_out(const at::Scalar & start, const at::Tensor & end, int64_t steps, at::Tensor & out); // {"schema": "aten::linspace.Scalar_Tensor_out(Scalar start, Tensor end, int steps, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor log(const at::Tensor & self); // {"schema": "aten::log(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & log_(at::Tensor & self); // {"schema": "aten::log_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & log_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::log.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor log10(const at::Tensor & self); // {"schema": "aten::log10(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & log10_(at::Tensor & self); // {"schema": "aten::log10_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & log10_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::log10.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor log1p(const at::Tensor & self); // {"schema": "aten::log1p(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & log1p_(at::Tensor & self); // {"schema": "aten::log1p_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & log1p_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor log2(const at::Tensor & self); // {"schema": "aten::log2(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & log2_(at::Tensor & self); // {"schema": "aten::log2_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & log2_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::log2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & logaddexp_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::logaddexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor logaddexp(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::logaddexp(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & logaddexp2_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::logaddexp2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor logaddexp2(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::logaddexp2(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor xlogy(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::xlogy.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor xlogy(const at::Scalar & self, const at::Tensor & other); // {"schema": "aten::xlogy.Scalar_Self(Scalar self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor xlogy(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::xlogy.Scalar_Other(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & xlogy_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::xlogy_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & xlogy_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::xlogy_.Scalar_Other(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & xlogy_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & xlogy_out(const at::Scalar & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & xlogy_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor logspace(const at::Scalar & start, const at::Scalar & end, int64_t steps, double base, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "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", "dispatch": "True", "default": "True"}
at::Tensor logspace(const at::Tensor & start, const at::Tensor & end, int64_t steps, double base, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "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", "dispatch": "True", "default": "True"}
at::Tensor logspace(const at::Tensor & start, const at::Scalar & end, int64_t steps, double base, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "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", "dispatch": "True", "default": "True"}
at::Tensor logspace(const at::Scalar & start, const at::Tensor & end, int64_t steps, double base, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "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", "dispatch": "True", "default": "True"}
at::Tensor & logspace_out(const at::Scalar & start, const at::Scalar & end, int64_t steps, double base, at::Tensor & out); // {"schema": "aten::logspace.out(Scalar start, Scalar end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & logspace_out(const at::Tensor & start, const at::Tensor & end, int64_t steps, double base, at::Tensor & out); // {"schema": "aten::logspace.Tensor_Tensor_out(Tensor start, Tensor end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & logspace_out(const at::Tensor & start, const at::Scalar & end, int64_t steps, double base, at::Tensor & out); // {"schema": "aten::logspace.Tensor_Scalar_out(Tensor start, Scalar end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & logspace_out(const at::Scalar & start, const at::Tensor & end, int64_t steps, double base, at::Tensor & out); // {"schema": "aten::logspace.Scalar_Tensor_out(Scalar start, Tensor end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor log_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & log_softmax_out(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::log_softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor _log_softmax(const at::Tensor & self, int64_t dim, bool half_to_float); // {"schema": "aten::_log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _log_softmax_out(const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out); // {"schema": "aten::_log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _log_softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype); // {"schema": "aten::_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _log_softmax_backward_data_out(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, at::Tensor & out); // {"schema": "aten::_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _logcumsumexp(const at::Tensor & self, int64_t dim); // {"schema": "aten::_logcumsumexp(Tensor self, int dim) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & _logcumsumexp_out(const at::Tensor & self, int64_t dim, at::Tensor & out); // {"schema": "aten::_logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor logcumsumexp(const at::Tensor & self, int64_t dim); // {"schema": "aten::logcumsumexp(Tensor self, int dim) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & logcumsumexp_out(const at::Tensor & self, int64_t dim, at::Tensor & out); // {"schema": "aten::logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor logsumexp(const at::Tensor & self, at::IntArrayRef dim, bool keepdim); // {"schema": "aten::logsumexp(Tensor self, int[1] dim, bool keepdim=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & logsumexp_out(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out); // {"schema": "aten::logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor margin_ranking_loss(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin, int64_t reduction); // {"schema": "aten::margin_ranking_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor matmul(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::matmul(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> matmul_backward(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & other, ::std::array<bool,2> mask); // {"schema": "aten::matmul_backward(Tensor grad, Tensor self, Tensor other, bool[2] mask) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor & matmul_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor matrix_power(const at::Tensor & self, int64_t n); // {"schema": "aten::matrix_power(Tensor self, int n) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & matrix_power_out(const at::Tensor & self, int64_t n, at::Tensor & out); // {"schema": "aten::matrix_power.out(Tensor self, int n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor matrix_exp(const at::Tensor & self); // {"schema": "aten::matrix_exp(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor matrix_exp_backward(const at::Tensor & self, const at::Tensor & grad); // {"schema": "aten::matrix_exp_backward(Tensor self, Tensor grad) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> _aminmax(const at::Tensor & self); // {"schema": "aten::_aminmax(Tensor self) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _aminmax(const at::Tensor & self, int64_t dim, bool keepdim); // {"schema": "aten::_aminmax.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> aminmax(const at::Tensor & self, ::std::optional<int64_t> dim, bool keepdim); // {"schema": "aten::aminmax(Tensor self, *, int? dim=None, bool keepdim=False) -> (Tensor min, Tensor max)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> aminmax_out(const at::Tensor & self, ::std::optional<int64_t> dim, bool keepdim, at::Tensor & min, at::Tensor & max); // {"schema": "aten::aminmax.out(Tensor self, *, int? dim=None, bool keepdim=False, Tensor(a!) min, Tensor(b!) max) -> (Tensor(a!) min, Tensor(b!) max)", "dispatch": "True", "default": "False"}
at::Tensor _compute_linear_combination(const at::Tensor & input, const at::Tensor & coefficients); // {"schema": "aten::_compute_linear_combination(Tensor input, Tensor coefficients) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & _compute_linear_combination_out(const at::Tensor & input, const at::Tensor & coefficients, at::Tensor & out); // {"schema": "aten::_compute_linear_combination.out(Tensor input, Tensor coefficients, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> max(const at::Tensor & self, int64_t dim, bool keepdim); // {"schema": "aten::max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> max_out(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & max, at::Tensor & max_values); // {"schema": "aten::max.dim_max(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices)", "dispatch": "True", "default": "False"}
at::Tensor value_selecting_reduction_backward(const at::Tensor & grad, int64_t dim, const at::Tensor & indices, c10::SymIntArrayRef sizes, bool keepdim); // {"schema": "aten::value_selecting_reduction_backward(Tensor grad, int dim, Tensor indices, SymInt[] sizes, bool keepdim) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor amax(const at::Tensor & self, at::IntArrayRef dim, bool keepdim); // {"schema": "aten::amax(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & amax_out(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out); // {"schema": "aten::amax.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> max_pool1d_with_indices(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode); // {"schema": "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)", "dispatch": "False", "default": "True"}
at::Tensor max_pool1d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor max_pool2d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode); // {"schema": "aten::max_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor mkldnn_max_pool2d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode); // {"schema": "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", "dispatch": "True", "default": "False"}
at::Tensor mkldnn_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode); // {"schema": "aten::mkldnn_max_pool2d_backward(Tensor grad_output, Tensor output, Tensor input, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor mkldnn_max_pool3d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode); // {"schema": "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", "dispatch": "True", "default": "False"}
at::Tensor mkldnn_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode); // {"schema": "aten::mkldnn_max_pool3d_backward(Tensor grad_output, Tensor output, Tensor input, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor quantized_max_pool1d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode); // {"schema": "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", "dispatch": "True", "default": "False"}
at::Tensor quantized_max_pool2d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode); // {"schema": "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", "dispatch": "True", "default": "False"}
at::Tensor quantized_max_pool3d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode); // {"schema": "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", "dispatch": "True", "default": "False"}
at::Tensor max_pool3d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor mean(const at::Tensor & self, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::mean(Tensor self, *, ScalarType? dtype=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & mean_out(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::mean.dtype_out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor mean(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::mean.dim(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & mean_out(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::mean.out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor nanmean(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::nanmean(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & nanmean_out(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::nanmean.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor median(const at::Tensor & self); // {"schema": "aten::median(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> median(const at::Tensor & self, int64_t dim, bool keepdim); // {"schema": "aten::median.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> median_out(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices); // {"schema": "aten::median.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)", "dispatch": "True", "default": "False"}
at::Tensor nanmedian(const at::Tensor & self); // {"schema": "aten::nanmedian(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> nanmedian(const at::Tensor & self, int64_t dim, bool keepdim); // {"schema": "aten::nanmedian.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> nanmedian_out(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices); // {"schema": "aten::nanmedian.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> min(const at::Tensor & self, int64_t dim, bool keepdim); // {"schema": "aten::min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> min_out(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & min, at::Tensor & min_indices); // {"schema": "aten::min.dim_min(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices)", "dispatch": "True", "default": "False"}
at::Tensor amin(const at::Tensor & self, at::IntArrayRef dim, bool keepdim); // {"schema": "aten::amin(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & amin_out(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out); // {"schema": "aten::amin.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _mps_convolution(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups); // {"schema": "aten::_mps_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> mps_convolution_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,3> output_mask); // {"schema": "aten::mps_convolution_backward(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor mkldnn_convolution(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups); // {"schema": "aten::mkldnn_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> 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); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> mkldnn_rnn_layer_backward(const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace); // {"schema": "aten::mkldnn_rnn_layer_backward(Tensor input, Tensor weight1, Tensor weight2, Tensor weight3, Tensor weight4, Tensor hx_, Tensor cx_tmp, Tensor output, Tensor hy_, Tensor cy_, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, bool reverse, int mode, int hidden_size, int num_layers, bool has_biases, bool train, bool bidirectional, int[] batch_sizes, bool batch_first, Tensor workspace) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> miopen_batch_norm(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double exponential_average_factor, double epsilon); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> miopen_batch_norm_backward(const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_var, double epsilon); // {"schema": "aten::miopen_batch_norm_backward(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor miopen_convolution(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic); // {"schema": "aten::miopen_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor miopen_convolution_transpose(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic); // {"schema": "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", "dispatch": "True", "default": "False"}
at::Tensor miopen_depthwise_convolution(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic); // {"schema": "aten::miopen_depthwise_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor miopen_convolution_relu(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups); // {"schema": "aten::miopen_convolution_relu(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor miopen_convolution_add_relu(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups); // {"schema": "aten::miopen_convolution_add_relu(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> miopen_rnn(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & hx, const ::std::optional<at::Tensor> & 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<at::Tensor> & dropout_state); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,::std::vector<at::Tensor>> miopen_rnn_backward(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, 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<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask); // {"schema": "aten::miopen_rnn_backward(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask) -> (Tensor, Tensor, Tensor, Tensor[])", "dispatch": "True", "default": "False"}
bool _use_miopen_ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank); // {"schema": "aten::_use_miopen_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank) -> bool", "dispatch": "True", "default": "False"}
bool _use_miopen_ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank); // {"schema": "aten::_use_miopen_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank) -> bool", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> miopen_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); // {"schema": "aten::miopen_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank, bool deterministic, bool zero_infinity) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> miopen_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); // {"schema": "aten::miopen_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank, bool deterministic, bool zero_infinity) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor mm(const at::Tensor & self, const at::Tensor & mat2); // {"schema": "aten::mm(Tensor self, Tensor mat2) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & mm_out(const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out); // {"schema": "aten::mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor mm(const at::Tensor & self, const at::Tensor & mat2, at::ScalarType out_dtype); // {"schema": "aten::mm.dtype(Tensor self, Tensor mat2, ScalarType out_dtype) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & mm_out(const at::Tensor & self, const at::Tensor & mat2, at::ScalarType out_dtype, at::Tensor & out); // {"schema": "aten::mm.dtype_out(Tensor self, Tensor mat2, ScalarType out_dtype, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _int_mm(const at::Tensor & self, const at::Tensor & mat2); // {"schema": "aten::_int_mm(Tensor self, Tensor mat2) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & _int_mm_out(const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out); // {"schema": "aten::_int_mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _convert_weight_to_int4pack(const at::Tensor & self, int64_t innerKTiles); // {"schema": "aten::_convert_weight_to_int4pack(Tensor self, int innerKTiles) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _weight_int4pack_mm(const at::Tensor & self, const at::Tensor & mat2, int64_t qGroupSize, const at::Tensor & qScaleAndZeros); // {"schema": "aten::_weight_int4pack_mm(Tensor self, Tensor mat2, int qGroupSize, Tensor qScaleAndZeros) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _weight_int4pack_mm_with_scales_and_zeros(const at::Tensor & self, const at::Tensor & mat2, int64_t qGroupSize, const at::Tensor & qScale, const at::Tensor & qZeros); // {"schema": "aten::_weight_int4pack_mm_with_scales_and_zeros(Tensor self, Tensor mat2, int qGroupSize, Tensor qScale, Tensor qZeros) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _convert_weight_to_int4pack_for_cpu(const at::Tensor & self, int64_t innerKTiles); // {"schema": "aten::_convert_weight_to_int4pack_for_cpu(Tensor self, int innerKTiles) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _weight_int4pack_mm_for_cpu(const at::Tensor & self, const at::Tensor & mat2, int64_t qGroupSize, const at::Tensor & qScaleAndZeros); // {"schema": "aten::_weight_int4pack_mm_for_cpu(Tensor self, Tensor mat2, int qGroupSize, Tensor qScaleAndZeros) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _dyn_quant_pack_4bit_weight(const at::Tensor & weights, const at::Tensor & scales_zeros, const ::std::optional<at::Tensor> & bias, int64_t block_size, int64_t in_features, int64_t out_features); // {"schema": "aten::_dyn_quant_pack_4bit_weight(Tensor weights, Tensor scales_zeros, Tensor? bias, int block_size, int in_features, int out_features) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _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); // {"schema": "aten::_dyn_quant_matmul_4bit(Tensor inp, Tensor packed_weights, int block_size, int in_features, int out_features) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _weight_int8pack_mm(const at::Tensor & self, const at::Tensor & mat2, const at::Tensor & scales); // {"schema": "aten::_weight_int8pack_mm(Tensor self, Tensor mat2, Tensor scales) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _sparse_mm(const at::Tensor & sparse, const at::Tensor & dense); // {"schema": "aten::_sparse_mm(Tensor sparse, Tensor dense) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _sparse_mm(const at::Tensor & sparse, const at::Tensor & dense, c10::string_view reduce); // {"schema": "aten::_sparse_mm.reduce(Tensor sparse, Tensor dense, str reduce) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _sparse_sparse_matmul(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::_sparse_sparse_matmul(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> mode(const at::Tensor & self, int64_t dim, bool keepdim); // {"schema": "aten::mode(Tensor self, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor &,at::Tensor &> mode_out(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices); // {"schema": "aten::mode.values(Tensor self, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)", "dispatch": "True", "default": "True"}
at::Tensor mul(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::mul.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & mul_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::mul_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & mul_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::mul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor mul(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::mul.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & mul_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::mul_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor multiply(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::multiply.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & multiply_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::multiply_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & multiply_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::multiply.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor multiply(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::multiply.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & multiply_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::multiply_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor mv(const at::Tensor & self, const at::Tensor & vec); // {"schema": "aten::mv(Tensor self, Tensor vec) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & mv_out(const at::Tensor & self, const at::Tensor & vec, at::Tensor & out); // {"schema": "aten::mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & mvlgamma_out(const at::Tensor & self, int64_t p, at::Tensor & out); // {"schema": "aten::mvlgamma.out(Tensor self, int p, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor mvlgamma(const at::Tensor & self, int64_t p); // {"schema": "aten::mvlgamma(Tensor self, int p) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & mvlgamma_(at::Tensor & self, int64_t p); // {"schema": "aten::mvlgamma_(Tensor(a!) self, int p) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor narrow_copy(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length); // {"schema": "aten::narrow_copy(Tensor self, int dim, SymInt start, SymInt length) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & narrow_copy_out(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length, at::Tensor & out); // {"schema": "aten::narrow_copy.out(Tensor self, int dim, SymInt start, SymInt length, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor narrow(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length); // {"schema": "aten::narrow(Tensor(a) self, int dim, SymInt start, SymInt length) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor narrow(const at::Tensor & self, int64_t dim, const at::Tensor & start, c10::SymInt length); // {"schema": "aten::narrow.Tensor(Tensor(a) self, int dim, Tensor start, SymInt length) -> Tensor(a)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_batch_norm(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double momentum, double eps); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_batch_norm_out(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd); // {"schema": "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!))", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _native_batch_norm_legit(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, bool training, double momentum, double eps); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _native_batch_norm_legit_no_training(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum, double eps); // {"schema": "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)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _native_batch_norm_legit_out(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd); // {"schema": "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!))", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _native_batch_norm_legit(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, bool training, double momentum, double eps); // {"schema": "aten::_native_batch_norm_legit.no_stats(Tensor input, Tensor? weight, Tensor? bias, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _native_batch_norm_legit_out(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd); // {"schema": "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!))", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> batch_norm_stats(const at::Tensor & input, double eps); // {"schema": "aten::batch_norm_stats(Tensor input, float eps) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor batch_norm_elemt(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & mean, const at::Tensor & invstd, double eps); // {"schema": "aten::batch_norm_elemt(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & batch_norm_elemt_out(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & mean, const at::Tensor & invstd, double eps, at::Tensor & out); // {"schema": "aten::batch_norm_elemt.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> batch_norm_gather_stats(const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum, double eps, int64_t count); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> batch_norm_gather_stats_with_counts(const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum, double eps, const at::Tensor & counts); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_batch_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_invstd, bool train, double eps, ::std::array<bool,3> output_mask); // {"schema": "aten::native_batch_norm_backward(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> batch_norm_backward_reduce(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & weight, bool input_g, bool weight_g, bool bias_g); // {"schema": "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)", "dispatch": "True", "default": "False"}
at::Tensor batch_norm_backward_elemt(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & weight, const at::Tensor & sum_dy, const at::Tensor & sum_dy_xmu, const at::Tensor & count); // {"schema": "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", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> batch_norm_update_stats(const at::Tensor & input, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum); // {"schema": "aten::batch_norm_update_stats(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
bool is_vulkan_available(); // {"schema": "aten::is_vulkan_available() -> bool", "dispatch": "False", "default": "True"}
bool _nnpack_available(); // {"schema": "aten::_nnpack_available() -> bool", "dispatch": "False", "default": "True"}
at::Tensor _nnpack_spatial_convolution(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride); // {"schema": "aten::_nnpack_spatial_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, SymInt[2] stride=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor ones(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & ones_out(c10::SymIntArrayRef size, at::Tensor & out); // {"schema": "aten::ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor ones_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::ones_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor pairwise_distance(const at::Tensor & x1, const at::Tensor & x2, double p, double eps, bool keepdim); // {"schema": "aten::pairwise_distance(Tensor x1, Tensor x2, float p=2, float eps=1e-06, bool keepdim=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor cdist(const at::Tensor & x1, const at::Tensor & x2, double p, ::std::optional<int64_t> compute_mode); // {"schema": "aten::cdist(Tensor x1, Tensor x2, float p=2, int? compute_mode=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _euclidean_dist(const at::Tensor & x1, const at::Tensor & x2); // {"schema": "aten::_euclidean_dist(Tensor x1, Tensor x2) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _cdist_forward(const at::Tensor & x1, const at::Tensor & x2, double p, ::std::optional<int64_t> compute_mode); // {"schema": "aten::_cdist_forward(Tensor x1, Tensor x2, float p, int? compute_mode) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _cdist_backward(const at::Tensor & grad, const at::Tensor & x1, const at::Tensor & x2, double p, const at::Tensor & cdist); // {"schema": "aten::_cdist_backward(Tensor grad, Tensor x1, Tensor x2, float p, Tensor cdist) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor pdist(const at::Tensor & self, double p); // {"schema": "aten::pdist(Tensor self, float p=2) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _pdist_forward(const at::Tensor & self, double p); // {"schema": "aten::_pdist_forward(Tensor self, float p=2) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _pdist_backward(const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist); // {"schema": "aten::_pdist_backward(Tensor grad, Tensor self, float p, Tensor pdist) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor cosine_similarity(const at::Tensor & x1, const at::Tensor & x2, int64_t dim, double eps); // {"schema": "aten::cosine_similarity(Tensor x1, Tensor x2, int dim=1, float eps=1e-08) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor permute(const at::Tensor & self, at::IntArrayRef dims); // {"schema": "aten::permute(Tensor(a) self, int[] dims) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor movedim(const at::Tensor & self, at::IntArrayRef source, at::IntArrayRef destination); // {"schema": "aten::movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor movedim(const at::Tensor & self, int64_t source, int64_t destination); // {"schema": "aten::movedim.int(Tensor(a) self, int source, int destination) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor moveaxis(const at::Tensor & self, at::IntArrayRef source, at::IntArrayRef destination); // {"schema": "aten::moveaxis.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor moveaxis(const at::Tensor & self, int64_t source, int64_t destination); // {"schema": "aten::moveaxis.int(Tensor(a) self, int source, int destination) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor numpy_T(const at::Tensor & self); // {"schema": "aten::numpy_T(Tensor(a) self) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor matrix_H(const at::Tensor & self); // {"schema": "aten::matrix_H(Tensor(a) self) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor mT(const at::Tensor & self); // {"schema": "aten::mT(Tensor(a) self) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor mH(const at::Tensor & self); // {"schema": "aten::mH(Tensor(a) self) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor adjoint(const at::Tensor & self); // {"schema": "aten::adjoint(Tensor(a) self) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor pixel_shuffle(const at::Tensor & self, int64_t upscale_factor); // {"schema": "aten::pixel_shuffle(Tensor self, int upscale_factor) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor pixel_unshuffle(const at::Tensor & self, int64_t downscale_factor); // {"schema": "aten::pixel_unshuffle(Tensor self, int downscale_factor) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor channel_shuffle(const at::Tensor & self, c10::SymInt groups); // {"schema": "aten::channel_shuffle(Tensor self, SymInt groups) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor native_channel_shuffle(const at::Tensor & self, c10::SymInt groups); // {"schema": "aten::native_channel_shuffle(Tensor self, SymInt groups) -> Tensor", "dispatch": "True", "default": "True"}
bool is_pinned(const at::Tensor & self, ::std::optional<at::Device> device); // {"schema": "aten::is_pinned(Tensor self, Device? device=None) -> bool", "dispatch": "True", "default": "True"}
at::Tensor pin_memory(const at::Tensor & self, ::std::optional<at::Device> device); // {"schema": "aten::pin_memory(Tensor(a) self, Device? device=None) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor _pin_memory(const at::Tensor & self, ::std::optional<at::Device> device); // {"schema": "aten::_pin_memory(Tensor self, Device? device=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor pinverse(const at::Tensor & self, double rcond); // {"schema": "aten::pinverse(Tensor self, float rcond=1e-15) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor poisson_nll_loss(const at::Tensor & input, const at::Tensor & target, bool log_input, bool full, double eps, int64_t reduction); // {"schema": "aten::poisson_nll_loss(Tensor input, Tensor target, bool log_input, bool full, float eps, int reduction) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor rad2deg(const at::Tensor & self); // {"schema": "aten::rad2deg(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & rad2deg_(at::Tensor & self); // {"schema": "aten::rad2deg_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & rad2deg_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::rad2deg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor deg2rad(const at::Tensor & self); // {"schema": "aten::deg2rad(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & deg2rad_(at::Tensor & self); // {"schema": "aten::deg2rad_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & deg2rad_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::deg2rad.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor scalar_tensor(const at::Scalar & s, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::scalar_tensor(Scalar s, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor rand(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor rand(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & rand_out(c10::SymIntArrayRef size, at::Tensor & out); // {"schema": "aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & rand_out(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor rand_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::rand_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor rand_like(const at::Tensor & self, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::rand_like.generator(Tensor self, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor randint(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::randint(SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor randint(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::randint.generator(SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor randint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::randint.low(SymInt low, SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor randint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "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", "dispatch": "True", "default": "True"}
at::Tensor & randint_out(c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out); // {"schema": "aten::randint.out(SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & randint_out(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::randint.generator_out(SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & randint_out(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out); // {"schema": "aten::randint.low_out(SymInt low, SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & randint_out(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::randint.low_generator_out(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor randint_like(const at::Tensor & self, c10::SymInt high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "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", "dispatch": "True", "default": "True"}
at::Tensor randint_like(const at::Tensor & self, c10::SymInt high, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::randint_like.generator(Tensor self, SymInt high, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor randint_like(const at::Tensor & self, const at::Tensor & high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::randint_like.Tensor(Tensor self, Tensor high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor randint_like(const at::Tensor & self, const at::Tensor & high, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::randint_like.Tensor_generator(Tensor self, Tensor high, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor randint_like(const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "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", "dispatch": "True", "default": "True"}
at::Tensor randint_like(const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::randint_like.low_generator_dtype(Tensor self, SymInt low, SymInt high, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor randn(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor randn(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::randn.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & randn_out(c10::SymIntArrayRef size, at::Tensor & out); // {"schema": "aten::randn.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & randn_out(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::randn.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor randn_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::randn_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor randn_like(const at::Tensor & self, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::randn_like.generator(Tensor self, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor randperm(c10::SymInt n, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::randperm(SymInt n, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor randperm(c10::SymInt n, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::randperm.generator(SymInt n, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & randperm_out(c10::SymInt n, at::Tensor & out); // {"schema": "aten::randperm.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & randperm_out(c10::SymInt n, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::randperm.generator_out(SymInt n, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor range(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::range.step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor range(const at::Scalar & start, const at::Scalar & end, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::range(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & range_out(const at::Scalar & start, const at::Scalar & end, at::Tensor & out); // {"schema": "aten::range.out_(Scalar start, Scalar end, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & range_out(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::Tensor & out); // {"schema": "aten::range.out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor ravel(const at::Tensor & self); // {"schema": "aten::ravel(Tensor(a) self) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor reciprocal(const at::Tensor & self); // {"schema": "aten::reciprocal(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & reciprocal_(at::Tensor & self); // {"schema": "aten::reciprocal_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & reciprocal_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::reciprocal.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor neg(const at::Tensor & self); // {"schema": "aten::neg(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & neg_(at::Tensor & self); // {"schema": "aten::neg_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & neg_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::neg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor negative(const at::Tensor & self); // {"schema": "aten::negative(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & negative_(at::Tensor & self); // {"schema": "aten::negative_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & negative_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::negative.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor repeat(const at::Tensor & self, c10::SymIntArrayRef repeats); // {"schema": "aten::repeat(Tensor self, SymInt[] repeats) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor repeat_interleave(const at::Tensor & repeats, ::std::optional<c10::SymInt> output_size); // {"schema": "aten::repeat_interleave.Tensor(Tensor repeats, *, SymInt? output_size=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor repeat_interleave(const at::Tensor & self, const at::Tensor & repeats, ::std::optional<int64_t> dim, ::std::optional<c10::SymInt> output_size); // {"schema": "aten::repeat_interleave.self_Tensor(Tensor self, Tensor repeats, int? dim=None, *, SymInt? output_size=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor repeat_interleave(const at::Tensor & self, c10::SymInt repeats, ::std::optional<int64_t> dim, ::std::optional<c10::SymInt> output_size); // {"schema": "aten::repeat_interleave.self_int(Tensor self, SymInt repeats, int? dim=None, *, SymInt? output_size=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor reshape(const at::Tensor & self, c10::SymIntArrayRef shape); // {"schema": "aten::reshape(Tensor(a) self, SymInt[] shape) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor _reshape_copy(const at::Tensor & self, c10::SymIntArrayRef size); // {"schema": "aten::_reshape_copy(Tensor self, SymInt[] size) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _reshape_alias(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride); // {"schema": "aten::_reshape_alias(Tensor(a) self, SymInt[] size, SymInt[] stride) -> Tensor(a)", "dispatch": "True", "default": "False"}
at::Tensor _mkldnn_reshape(const at::Tensor & self, at::IntArrayRef shape); // {"schema": "aten::_mkldnn_reshape(Tensor self, int[] shape) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor reshape_as(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::reshape_as(Tensor(a) self, Tensor other) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor round(const at::Tensor & self); // {"schema": "aten::round(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & round_(at::Tensor & self); // {"schema": "aten::round_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & round_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor round(const at::Tensor & self, int64_t decimals); // {"schema": "aten::round.decimals(Tensor self, *, int decimals) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & round_(at::Tensor & self, int64_t decimals); // {"schema": "aten::round_.decimals(Tensor(a!) self, *, int decimals) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & round_out(const at::Tensor & self, int64_t decimals, at::Tensor & out); // {"schema": "aten::round.decimals_out(Tensor self, *, int decimals, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor rrelu(const at::Tensor & self, const at::Scalar & lower, const at::Scalar & upper, bool training, ::std::optional<at::Generator> generator); // {"schema": "aten::rrelu(Tensor self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & rrelu_(at::Tensor & self, const at::Scalar & lower, const at::Scalar & upper, bool training, ::std::optional<at::Generator> generator); // {"schema": "aten::rrelu_(Tensor(a!) self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor relu(const at::Tensor & self); // {"schema": "aten::relu(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & relu_(at::Tensor & self); // {"schema": "aten::relu_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor relu6(const at::Tensor & self); // {"schema": "aten::relu6(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & relu6_(at::Tensor & self); // {"schema": "aten::relu6_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor prelu(const at::Tensor & self, const at::Tensor & weight); // {"schema": "aten::prelu(Tensor self, Tensor weight) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _prelu_kernel(const at::Tensor & self, const at::Tensor & weight); // {"schema": "aten::_prelu_kernel(Tensor self, Tensor weight) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _prelu_kernel_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight); // {"schema": "aten::_prelu_kernel_backward(Tensor grad_output, Tensor self, Tensor weight) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor & gelu_out(const at::Tensor & self, c10::string_view approximate, at::Tensor & out); // {"schema": "aten::gelu.out(Tensor self, *, str approximate='none', Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & gelu_(at::Tensor & self, c10::string_view approximate); // {"schema": "aten::gelu_(Tensor(a!) self, *, str approximate='none') -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor gelu(const at::Tensor & self, c10::string_view approximate); // {"schema": "aten::gelu(Tensor self, *, str approximate='none') -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & gelu_backward_out(const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate, at::Tensor & grad_input); // {"schema": "aten::gelu_backward.grad_input(Tensor grad_output, Tensor self, *, str approximate='none', Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor gelu_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate); // {"schema": "aten::gelu_backward(Tensor grad_output, Tensor self, *, str approximate='none') -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor infinitely_differentiable_gelu_backward(const at::Tensor & grad, const at::Tensor & self); // {"schema": "aten::infinitely_differentiable_gelu_backward(Tensor grad, Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & hardshrink_out(const at::Tensor & self, const at::Scalar & lambd, at::Tensor & out); // {"schema": "aten::hardshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor hardshrink(const at::Tensor & self, const at::Scalar & lambd); // {"schema": "aten::hardshrink(Tensor self, Scalar lambd=0.5) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & hardshrink_backward_out(const at::Tensor & grad_out, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & grad_input); // {"schema": "aten::hardshrink_backward.grad_input(Tensor grad_out, Tensor self, Scalar lambd, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor hardshrink_backward(const at::Tensor & grad_out, const at::Tensor & self, const at::Scalar & lambd); // {"schema": "aten::hardshrink_backward(Tensor grad_out, Tensor self, Scalar lambd) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor rsqrt(const at::Tensor & self); // {"schema": "aten::rsqrt(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & rsqrt_(at::Tensor & self); // {"schema": "aten::rsqrt_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & rsqrt_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::rsqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor select(const at::Tensor & self, int64_t dim, c10::SymInt index); // {"schema": "aten::select.int(Tensor(a) self, int dim, SymInt index) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor select_backward(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index); // {"schema": "aten::select_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _nested_select_backward(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim, c10::SymInt index); // {"schema": "aten::_nested_select_backward(Tensor grad_output, Tensor self, int dim, SymInt index) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor selu(const at::Tensor & self); // {"schema": "aten::selu(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & selu_(at::Tensor & self); // {"schema": "aten::selu_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor celu(const at::Tensor & self, const at::Scalar & alpha); // {"schema": "aten::celu(Tensor self, Scalar alpha=1.0) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & celu_(at::Tensor & self, const at::Scalar & alpha); // {"schema": "aten::celu_(Tensor(a!) self, Scalar alpha=1.0) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor silu(const at::Tensor & self); // {"schema": "aten::silu(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & silu_(at::Tensor & self); // {"schema": "aten::silu_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & silu_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::silu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & silu_backward_out(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input); // {"schema": "aten::silu_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor silu_backward(const at::Tensor & grad_output, const at::Tensor & self); // {"schema": "aten::silu_backward(Tensor grad_output, Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor mish(const at::Tensor & self); // {"schema": "aten::mish(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & mish_(at::Tensor & self); // {"schema": "aten::mish_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & mish_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::mish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor mish_backward(const at::Tensor & grad_output, const at::Tensor & self); // {"schema": "aten::mish_backward(Tensor grad_output, Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor sigmoid(const at::Tensor & self); // {"schema": "aten::sigmoid(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & sigmoid_(at::Tensor & self); // {"schema": "aten::sigmoid_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & sigmoid_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor logit(const at::Tensor & self, ::std::optional<double> eps); // {"schema": "aten::logit(Tensor self, float? eps=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & logit_(at::Tensor & self, ::std::optional<double> eps); // {"schema": "aten::logit_(Tensor(a!) self, float? eps=None) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & logit_out(const at::Tensor & self, ::std::optional<double> eps, at::Tensor & out); // {"schema": "aten::logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor sin(const at::Tensor & self); // {"schema": "aten::sin(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & sin_(at::Tensor & self); // {"schema": "aten::sin_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & sin_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::sin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor sinc(const at::Tensor & self); // {"schema": "aten::sinc(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & sinc_(at::Tensor & self); // {"schema": "aten::sinc_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & sinc_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor sinh(const at::Tensor & self); // {"schema": "aten::sinh(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & sinh_(at::Tensor & self); // {"schema": "aten::sinh_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & sinh_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor detach(const at::Tensor & self); // {"schema": "aten::detach(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor & detach_(at::Tensor & self); // {"schema": "aten::detach_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
int64_t size(const at::Tensor & self, int64_t dim); // {"schema": "aten::size.int(Tensor self, int dim) -> int", "dispatch": "False", "default": "True"}
c10::SymInt sym_size(const at::Tensor & self, int64_t dim); // {"schema": "aten::sym_size.int(Tensor self, int dim) -> SymInt", "dispatch": "False", "default": "True"}
c10::SymBool sym_is_contiguous(const at::Tensor & self, at::MemoryFormat memory_format); // {"schema": "aten::sym_is_contiguous(Tensor self, MemoryFormat memory_format=contiguous_format) -> SymBool", "dispatch": "False", "default": "True"}
c10::SymInt sym_numel(const at::Tensor & self); // {"schema": "aten::sym_numel(Tensor self) -> SymInt", "dispatch": "False", "default": "True"}
c10::SymInt sym_storage_offset(const at::Tensor & self); // {"schema": "aten::sym_storage_offset(Tensor self) -> SymInt", "dispatch": "False", "default": "True"}
at::Tensor slice(const at::Tensor & self, int64_t dim, ::std::optional<c10::SymInt> start, ::std::optional<c10::SymInt> end, c10::SymInt step); // {"schema": "aten::slice.Tensor(Tensor(a) self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor slice_backward(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step); // {"schema": "aten::slice_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor slice_inverse(const at::Tensor & self, const at::Tensor & src, int64_t dim, ::std::optional<c10::SymInt> start, ::std::optional<c10::SymInt> end, c10::SymInt step); // {"schema": "aten::slice_inverse(Tensor(a) self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor slice_scatter(const at::Tensor & self, const at::Tensor & src, int64_t dim, ::std::optional<c10::SymInt> start, ::std::optional<c10::SymInt> end, c10::SymInt step); // {"schema": "aten::slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor select_scatter(const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::SymInt index); // {"schema": "aten::select_scatter(Tensor self, Tensor src, int dim, SymInt index) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor diagonal_scatter(const at::Tensor & self, const at::Tensor & src, int64_t offset, int64_t dim1, int64_t dim2); // {"schema": "aten::diagonal_scatter(Tensor self, Tensor src, int offset=0, int dim1=0, int dim2=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor as_strided_scatter(const at::Tensor & self, const at::Tensor & src, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset); // {"schema": "aten::as_strided_scatter(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor smm(const at::Tensor & self, const at::Tensor & mat2); // {"schema": "aten::smm(Tensor self, Tensor mat2) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & softmax_out(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor _softmax(const at::Tensor & self, int64_t dim, bool half_to_float); // {"schema": "aten::_softmax(Tensor self, int dim, bool half_to_float) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _softmax_out(const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out); // {"schema": "aten::_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype); // {"schema": "aten::_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _softmax_backward_data_out(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, at::Tensor & grad_input); // {"schema": "aten::_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
::std::vector<at::Tensor> unsafe_split(const at::Tensor & self, c10::SymInt split_size, int64_t dim); // {"schema": "aten::unsafe_split.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[]", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> split(const at::Tensor & self, c10::SymInt split_size, int64_t dim); // {"schema": "aten::split.Tensor(Tensor(a -> *) self, SymInt split_size, int dim=0) -> Tensor(a)[]", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> split(const at::Tensor & self, c10::SymIntArrayRef split_size, int64_t dim); // {"schema": "aten::split.sizes(Tensor(a -> *) self, SymInt[] split_size, int dim=0) -> Tensor(a)[]", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> unsafe_split_with_sizes(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim); // {"schema": "aten::unsafe_split_with_sizes(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[]", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> split_with_sizes(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim); // {"schema": "aten::split_with_sizes(Tensor(a -> *) self, SymInt[] split_sizes, int dim=0) -> Tensor(a)[]", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> hsplit(const at::Tensor & self, int64_t sections); // {"schema": "aten::hsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[]", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> hsplit(const at::Tensor & self, at::IntArrayRef indices); // {"schema": "aten::hsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[]", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> vsplit(const at::Tensor & self, int64_t sections); // {"schema": "aten::vsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[]", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> vsplit(const at::Tensor & self, at::IntArrayRef indices); // {"schema": "aten::vsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[]", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> dsplit(const at::Tensor & self, int64_t sections); // {"schema": "aten::dsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[]", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> dsplit(const at::Tensor & self, at::IntArrayRef indices); // {"schema": "aten::dsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[]", "dispatch": "False", "default": "True"}
at::Tensor squeeze(const at::Tensor & self); // {"schema": "aten::squeeze(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor squeeze(const at::Tensor & self, int64_t dim); // {"schema": "aten::squeeze.dim(Tensor(a) self, int dim) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor squeeze(const at::Tensor & self, at::IntArrayRef dim); // {"schema": "aten::squeeze.dims(Tensor(a) self, int[] dim) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor & squeeze_(at::Tensor & self); // {"schema": "aten::squeeze_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & squeeze_(at::Tensor & self, int64_t dim); // {"schema": "aten::squeeze_.dim(Tensor(a!) self, int dim) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & squeeze_(at::Tensor & self, at::IntArrayRef dim); // {"schema": "aten::squeeze_.dims(Tensor(a!) self, int[] dim) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor sspaddmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha); // {"schema": "aten::sspaddmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & sspaddmm_out(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::sspaddmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _chunk_cat(at::TensorList tensors, int64_t dim, int64_t num_chunks); // {"schema": "aten::_chunk_cat(Tensor[] tensors, int dim, int num_chunks) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _chunk_cat_out(at::TensorList tensors, int64_t dim, int64_t num_chunks, at::Tensor & out); // {"schema": "aten::_chunk_cat.out(Tensor[] tensors, int dim, int num_chunks, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor stack(at::TensorList tensors, int64_t dim); // {"schema": "aten::stack(Tensor[] tensors, int dim=0) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & stack_out(at::TensorList tensors, int64_t dim, at::Tensor & out); // {"schema": "aten::stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor _stack(at::TensorList tensors, int64_t dim); // {"schema": "aten::_stack(Tensor[] tensors, int dim=0) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _stack_out(at::TensorList tensors, int64_t dim, at::Tensor & out); // {"schema": "aten::_stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor hstack(at::TensorList tensors); // {"schema": "aten::hstack(Tensor[] tensors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & hstack_out(at::TensorList tensors, at::Tensor & out); // {"schema": "aten::hstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor vstack(at::TensorList tensors); // {"schema": "aten::vstack(Tensor[] tensors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & vstack_out(at::TensorList tensors, at::Tensor & out); // {"schema": "aten::vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor dstack(at::TensorList tensors); // {"schema": "aten::dstack(Tensor[] tensors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & dstack_out(at::TensorList tensors, at::Tensor & out); // {"schema": "aten::dstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor stft(const at::Tensor & self, int64_t n_fft, ::std::optional<int64_t> hop_length, ::std::optional<int64_t> win_length, const ::std::optional<at::Tensor> & window, bool normalized, ::std::optional<bool> onesided, ::std::optional<bool> return_complex, ::std::optional<bool> align_to_window); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor stft(const at::Tensor & self, int64_t n_fft, ::std::optional<int64_t> hop_length, ::std::optional<int64_t> win_length, const ::std::optional<at::Tensor> & window, bool center, c10::string_view pad_mode, bool normalized, ::std::optional<bool> onesided, ::std::optional<bool> return_complex, ::std::optional<bool> align_to_window); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor istft(const at::Tensor & self, int64_t n_fft, ::std::optional<int64_t> hop_length, ::std::optional<int64_t> win_length, const ::std::optional<at::Tensor> & window, bool center, bool normalized, ::std::optional<bool> onesided, ::std::optional<int64_t> length, bool return_complex); // {"schema": "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", "dispatch": "False", "default": "True"}
int64_t stride(const at::Tensor & self, int64_t dim); // {"schema": "aten::stride.int(Tensor self, int dim) -> int", "dispatch": "False", "default": "True"}
c10::SymInt sym_stride(const at::Tensor & self, int64_t dim); // {"schema": "aten::sym_stride.int(Tensor self, int dim) -> SymInt", "dispatch": "False", "default": "True"}
at::Tensor sum(const at::Tensor & self, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::sum(Tensor self, *, ScalarType? dtype=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor sum(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::sum.dim_IntList(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & sum_out(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::sum.IntList_out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _nested_sum_backward(const at::Tensor & grad, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim); // {"schema": "aten::_nested_sum_backward(Tensor grad, Tensor self, int[1]? dim, bool keepdim=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor nansum(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::nansum(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & nansum_out(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::nansum.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor hash_tensor(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, int64_t mode); // {"schema": "aten::hash_tensor(Tensor self, int[1] dim=[], *, bool keepdim=False, int mode=0) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & hash_tensor_out(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, int64_t mode, at::Tensor & out); // {"schema": "aten::hash_tensor.out(Tensor self, int[1] dim=[], *, bool keepdim=False, int mode=0, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor sum_to_size(const at::Tensor & self, c10::SymIntArrayRef size); // {"schema": "aten::sum_to_size(Tensor self, SymInt[] size) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor sqrt(const at::Tensor & self); // {"schema": "aten::sqrt(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & sqrt_(at::Tensor & self); // {"schema": "aten::sqrt_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & sqrt_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor square(const at::Tensor & self); // {"schema": "aten::square(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & square_(at::Tensor & self); // {"schema": "aten::square_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & square_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor std(const at::Tensor & self, bool unbiased); // {"schema": "aten::std(Tensor self, bool unbiased=True) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor std(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim); // {"schema": "aten::std.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor std(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim); // {"schema": "aten::std.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, bool unbiased); // {"schema": "aten::std_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim); // {"schema": "aten::std_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim); // {"schema": "aten::std_mean.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor & std_out(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out); // {"schema": "aten::std.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & std_out(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out); // {"schema": "aten::std.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor prod(const at::Tensor & self, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::prod(Tensor self, *, ScalarType? dtype=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor prod(const at::Tensor & self, int64_t dim, bool keepdim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::prod.dim_int(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & prod_out(const at::Tensor & self, int64_t dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::prod.int_out(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor t(const at::Tensor & self); // {"schema": "aten::t(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor & t_(at::Tensor & self); // {"schema": "aten::t_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor tan(const at::Tensor & self); // {"schema": "aten::tan(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & tan_(at::Tensor & self); // {"schema": "aten::tan_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & tan_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::tan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor tanh(const at::Tensor & self); // {"schema": "aten::tanh(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & tanh_(at::Tensor & self); // {"schema": "aten::tanh_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & tanh_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor tensordot(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other); // {"schema": "aten::tensordot(Tensor self, Tensor other, int[] dims_self, int[] dims_other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & tensordot_out(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other, at::Tensor & out); // {"schema": "aten::tensordot.out(Tensor self, Tensor other, int[] dims_self, int[] dims_other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor threshold(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value); // {"schema": "aten::threshold(Tensor self, Scalar threshold, Scalar value) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & threshold_(at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value); // {"schema": "aten::threshold_(Tensor(a!) self, Scalar threshold, Scalar value) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & threshold_out(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value, at::Tensor & out); // {"schema": "aten::threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & threshold_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold, at::Tensor & grad_input); // {"schema": "aten::threshold_backward.grad_input(Tensor grad_output, Tensor self, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor threshold_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold); // {"schema": "aten::threshold_backward(Tensor grad_output, Tensor self, Scalar threshold) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor tile(const at::Tensor & self, c10::SymIntArrayRef dims); // {"schema": "aten::tile(Tensor self, SymInt[] dims) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor transpose(const at::Tensor & self, int64_t dim0, int64_t dim1); // {"schema": "aten::transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor _mkldnn_transpose(const at::Tensor & self, int64_t dim0, int64_t dim1); // {"schema": "aten::_mkldnn_transpose(Tensor self, int dim0, int dim1) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & transpose_(at::Tensor & self, int64_t dim0, int64_t dim1); // {"schema": "aten::transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _mkldnn_transpose_(at::Tensor & self, int64_t dim0, int64_t dim1); // {"schema": "aten::_mkldnn_transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor one_hot(const at::Tensor & self, int64_t num_classes); // {"schema": "aten::one_hot(Tensor self, int num_classes=-1) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor flip(const at::Tensor & self, at::IntArrayRef dims); // {"schema": "aten::flip(Tensor self, int[] dims) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor fliplr(const at::Tensor & self); // {"schema": "aten::fliplr(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor flipud(const at::Tensor & self); // {"schema": "aten::flipud(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor roll(const at::Tensor & self, c10::SymIntArrayRef shifts, at::IntArrayRef dims); // {"schema": "aten::roll(Tensor self, SymInt[1] shifts, int[1] dims=[]) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor rot90(const at::Tensor & self, int64_t k, at::IntArrayRef dims); // {"schema": "aten::rot90(Tensor self, int k=1, int[] dims=[0,1]) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor trapezoid(const at::Tensor & y, const at::Tensor & x, int64_t dim); // {"schema": "aten::trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor trapezoid(const at::Tensor & y, const at::Scalar & dx, int64_t dim); // {"schema": "aten::trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor trapz(const at::Tensor & y, const at::Tensor & x, int64_t dim); // {"schema": "aten::trapz.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor trapz(const at::Tensor & y, double dx, int64_t dim); // {"schema": "aten::trapz.dx(Tensor y, *, float dx=1, int dim=-1) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _transform_bias_rescale_qkv(const at::Tensor & qkv, const at::Tensor & qkv_bias, int64_t num_heads); // {"schema": "aten::_transform_bias_rescale_qkv(Tensor qkv, Tensor qkv_bias, int num_heads) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor _nested_tensor_from_mask(const at::Tensor & t, const at::Tensor & mask, bool mask_check); // {"schema": "aten::_nested_tensor_from_mask(Tensor t, Tensor mask, bool mask_check=True) -> Tensor", "dispatch": "True", "default": "False"}
bool _nested_tensor_from_mask_left_aligned(const at::Tensor & t, const at::Tensor & mask); // {"schema": "aten::_nested_tensor_from_mask_left_aligned(Tensor t, Tensor mask) -> bool", "dispatch": "True", "default": "False"}
at::Tensor _nested_from_padded(const at::Tensor & padded, const at::Tensor & cpu_nested_shape_example, bool fuse_transform_0213); // {"schema": "aten::_nested_from_padded(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _nested_tensor_size(const at::Tensor & self); // {"schema": "aten::_nested_tensor_size(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _nested_tensor_strides(const at::Tensor & self); // {"schema": "aten::_nested_tensor_strides(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _nested_tensor_storage_offsets(const at::Tensor & self); // {"schema": "aten::_nested_tensor_storage_offsets(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _nested_from_padded_and_nested_example(const at::Tensor & padded, const at::Tensor & nt_example); // {"schema": "aten::_nested_from_padded_and_nested_example(Tensor padded, Tensor nt_example) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _nested_view_from_buffer(const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets); // {"schema": "aten::_nested_view_from_buffer(Tensor(a) self, Tensor nested_size, Tensor nested_strides, Tensor offsets) -> Tensor(a)", "dispatch": "True", "default": "False"}
at::Tensor _nested_view_from_buffer_copy(const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets); // {"schema": "aten::_nested_view_from_buffer_copy(Tensor self, Tensor nested_size, Tensor nested_strides, Tensor offsets) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _nested_view_from_jagged(const at::Tensor & self, const at::Tensor & offsets, const at::Tensor & dummy, const ::std::optional<at::Tensor> & lengths, int64_t ragged_idx, const ::std::optional<at::Tensor> & min_seqlen, const ::std::optional<at::Tensor> & max_seqlen); // {"schema": "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)", "dispatch": "True", "default": "False"}
at::Tensor _nested_view_from_jagged_copy(const at::Tensor & self, const at::Tensor & offsets, const at::Tensor & dummy, const ::std::optional<at::Tensor> & lengths, int64_t ragged_idx, const ::std::optional<at::Tensor> & min_seqlen, const ::std::optional<at::Tensor> & max_seqlen); // {"schema": "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", "dispatch": "True", "default": "True"}
at::Tensor _nested_get_values(const at::Tensor & self); // {"schema": "aten::_nested_get_values(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "False"}
at::Tensor _nested_get_values_copy(const at::Tensor & self); // {"schema": "aten::_nested_get_values_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _nested_get_offsets(const at::Tensor & self); // {"schema": "aten::_nested_get_offsets(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _nested_get_lengths(const at::Tensor & self); // {"schema": "aten::_nested_get_lengths(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
int64_t _nested_get_ragged_idx(const at::Tensor & self); // {"schema": "aten::_nested_get_ragged_idx(Tensor self) -> int", "dispatch": "True", "default": "False"}
at::Tensor _nested_get_min_seqlen(const at::Tensor & self); // {"schema": "aten::_nested_get_min_seqlen(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _nested_get_max_seqlen(const at::Tensor & self); // {"schema": "aten::_nested_get_max_seqlen(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _nested_get_jagged_dummy(const at::Tensor & any); // {"schema": "aten::_nested_get_jagged_dummy(Tensor any) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _nested_compute_contiguous_strides_offsets(const at::Tensor & nested_size); // {"schema": "aten::_nested_compute_contiguous_strides_offsets(Tensor nested_size) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor _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); // {"schema": "aten::_trilinear(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor triplet_margin_loss(const at::Tensor & anchor, const at::Tensor & positive, const at::Tensor & negative, double margin, double p, double eps, bool swap, int64_t reduction); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor trunc(const at::Tensor & self); // {"schema": "aten::trunc(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & trunc_(at::Tensor & self); // {"schema": "aten::trunc_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & trunc_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::trunc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor fix(const at::Tensor & self); // {"schema": "aten::fix(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fix_(at::Tensor & self); // {"schema": "aten::fix_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & fix_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::fix.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor type_as(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::type_as(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
bool _has_compatible_shallow_copy_type(const at::Tensor & self, const at::Tensor & from); // {"schema": "aten::_has_compatible_shallow_copy_type(Tensor self, Tensor from) -> bool", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> _unique(const at::Tensor & self, bool sorted, bool return_inverse); // {"schema": "aten::_unique(Tensor self, bool sorted=True, bool return_inverse=False) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim(const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts); // {"schema": "aten::unique_dim(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_consecutive(const at::Tensor & self, bool return_inverse, bool return_counts, ::std::optional<int64_t> dim); // {"schema": "aten::unique_consecutive(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim_consecutive(const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts); // {"schema": "aten::unique_dim_consecutive(Tensor self, int dim, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _unique2(const at::Tensor & self, bool sorted, bool return_inverse, bool return_counts); // {"schema": "aten::_unique2(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor _unsafe_view(const at::Tensor & self, c10::SymIntArrayRef size); // {"schema": "aten::_unsafe_view(Tensor self, SymInt[] size) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor unsqueeze(const at::Tensor & self, int64_t dim); // {"schema": "aten::unsqueeze(Tensor(a) self, int dim) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor & unsqueeze_(at::Tensor & self, int64_t dim); // {"schema": "aten::unsqueeze_(Tensor(a!) self, int dim) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor vander(const at::Tensor & x, ::std::optional<int64_t> N, bool increasing); // {"schema": "aten::vander(Tensor x, int? N=None, bool increasing=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor var(const at::Tensor & self, bool unbiased); // {"schema": "aten::var(Tensor self, bool unbiased=True) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor var(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim); // {"schema": "aten::var.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor var(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim); // {"schema": "aten::var.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & var_out(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out); // {"schema": "aten::var.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & var_out(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out); // {"schema": "aten::var.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, bool unbiased); // {"schema": "aten::var_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim); // {"schema": "aten::var_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim); // {"schema": "aten::var_mean.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor view_as(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::view_as(Tensor(a) self, Tensor other) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::where.self(Tensor condition, Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & where_out(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::where.self_out(Tensor condition, Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor where(const at::Tensor & condition, const at::Scalar & self, const at::Tensor & other); // {"schema": "aten::where.ScalarSelf(Tensor condition, Scalar self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::where.ScalarOther(Tensor condition, Tensor self, Scalar other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor where(const at::Tensor & condition, const at::Scalar & self, const at::Scalar & other); // {"schema": "aten::where.Scalar(Tensor condition, Scalar self, Scalar other) -> Tensor", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> where(const at::Tensor & condition); // {"schema": "aten::where(Tensor condition) -> Tensor[]", "dispatch": "False", "default": "True"}
at::Tensor norm_except_dim(const at::Tensor & v, int64_t pow, int64_t dim); // {"schema": "aten::norm_except_dim(Tensor v, int pow=2, int dim=0) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _weight_norm(const at::Tensor & v, const at::Tensor & g, int64_t dim); // {"schema": "aten::_weight_norm(Tensor v, Tensor g, int dim=0) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> _weight_norm_interface(const at::Tensor & v, const at::Tensor & g, int64_t dim); // {"schema": "aten::_weight_norm_interface(Tensor v, Tensor g, int dim=0) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _weight_norm_interface_backward(const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim); // {"schema": "aten::_weight_norm_interface_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _weight_norm_differentiable_backward(const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim); // {"schema": "aten::_weight_norm_differentiable_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor)", "dispatch": "False", "default": "True"}
at::Tensor _efficientzerotensor(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::_efficientzerotensor(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor zeros(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & zeros_out(c10::SymIntArrayRef size, at::Tensor & out); // {"schema": "aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor zeros_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _standard_gamma_grad(const at::Tensor & self, const at::Tensor & output); // {"schema": "aten::_standard_gamma_grad(Tensor self, Tensor output) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _standard_gamma(const at::Tensor & self, ::std::optional<at::Generator> generator); // {"schema": "aten::_standard_gamma(Tensor self, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _philox_key_split(const at::Tensor & key, int64_t num_splits); // {"schema": "aten::_philox_key_split(Tensor key, int num_splits) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _philox_key_fold_in(const at::Tensor & key, int64_t data); // {"schema": "aten::_philox_key_fold_in(Tensor key, int data) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & _philox_normal_(at::Tensor & self, const at::Tensor & key, double mean, double std); // {"schema": "aten::_philox_normal_(Tensor(a!) self, Tensor key, float mean=0, float std=1) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & _philox_uniform_(at::Tensor & self, const at::Tensor & key, double low, double high); // {"schema": "aten::_philox_uniform_(Tensor(a!) self, Tensor key, float low=0, float high=1) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _dirichlet_grad(const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total); // {"schema": "aten::_dirichlet_grad(Tensor x, Tensor alpha, Tensor total) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _sample_dirichlet(const at::Tensor & self, ::std::optional<at::Generator> generator); // {"schema": "aten::_sample_dirichlet(Tensor self, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor poisson(const at::Tensor & self, ::std::optional<at::Generator> generator); // {"schema": "aten::poisson(Tensor self, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor binomial(const at::Tensor & count, const at::Tensor & prob, ::std::optional<at::Generator> generator); // {"schema": "aten::binomial(Tensor count, Tensor prob, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor native_norm(const at::Tensor & self, const at::Scalar & p); // {"schema": "aten::native_norm(Tensor self, Scalar p=2) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor native_norm(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::native_norm.ScalarOpt_dim_dtype(Tensor self, Scalar? p, int[1] dim, bool keepdim, ScalarType? dtype) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _batch_norm_with_update(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, double momentum, double eps); // {"schema": "aten::_batch_norm_with_update(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _batch_norm_with_update_out(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd, at::Tensor & reserve); // {"schema": "aten::_batch_norm_with_update.out(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, float momentum, float eps, *, Tensor(d!) out, Tensor(e!) save_mean, Tensor(f!) save_invstd, Tensor(g!) reserve) -> (Tensor(d!), Tensor(e!), Tensor(f!), Tensor(g!))", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _batch_norm_no_update(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum, double eps); // {"schema": "aten::_batch_norm_no_update(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> batch_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_var, bool update, double eps, ::std::array<bool,3> output_mask, const at::Tensor & reserve); // {"schema": "aten::batch_norm_backward(Tensor grad_out, Tensor input, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, bool update, float eps, bool[3] output_mask, Tensor reserve) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor _sparse_sum(const at::Tensor & self); // {"schema": "aten::_sparse_sum(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _sparse_sum(const at::Tensor & self, at::ScalarType dtype); // {"schema": "aten::_sparse_sum.dtype(Tensor self, *, ScalarType dtype) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _sparse_sum(const at::Tensor & self, at::IntArrayRef dim); // {"schema": "aten::_sparse_sum.dim(Tensor self, int[1] dim) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _sparse_sum(const at::Tensor & self, at::IntArrayRef dim, at::ScalarType dtype); // {"schema": "aten::_sparse_sum.dim_dtype(Tensor self, int[1] dim, *, ScalarType dtype) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _sparse_sum_backward(const at::Tensor & grad, const at::Tensor & self, at::IntArrayRef dim); // {"schema": "aten::_sparse_sum_backward(Tensor grad, Tensor self, int[] dim) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _sparse_csr_sum(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::_sparse_csr_sum.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _sparse_csr_prod(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::_sparse_csr_prod.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _sparse_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::_sparse_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _sparse_softmax(const at::Tensor & self, int64_t dim, bool half_to_float); // {"schema": "aten::_sparse_softmax(Tensor self, int dim, bool half_to_float) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _sparse_softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self); // {"schema": "aten::_sparse_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _sparse_log_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::_sparse_log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _sparse_log_softmax(const at::Tensor & self, int64_t dim, bool half_to_float); // {"schema": "aten::_sparse_log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _sparse_log_softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self); // {"schema": "aten::_sparse_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _spdiags(const at::Tensor & diagonals, const at::Tensor & offsets, at::IntArrayRef shape, ::std::optional<at::Layout> layout); // {"schema": "aten::_spdiags(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor norm(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::ScalarType dtype); // {"schema": "aten::norm.ScalarOpt_dtype(Tensor self, Scalar? p, *, ScalarType dtype) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor norm(const at::Tensor & self, const at::Scalar & p); // {"schema": "aten::norm.Scalar(Tensor self, Scalar p=2) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor norm(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype); // {"schema": "aten::norm.ScalarOpt_dim_dtype(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor norm(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim); // {"schema": "aten::norm.ScalarOpt_dim(Tensor self, Scalar? p, int[1] dim, bool keepdim=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & norm_out(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype, at::Tensor & out); // {"schema": "aten::norm.dtype_out(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & norm_out(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::Tensor & out); // {"schema": "aten::norm.out(Tensor self, Scalar? p, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> frexp(const at::Tensor & self); // {"schema": "aten::frexp.Tensor(Tensor self) -> (Tensor mantissa, Tensor exponent)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> frexp_out(const at::Tensor & self, at::Tensor & mantissa, at::Tensor & exponent); // {"schema": "aten::frexp.Tensor_out(Tensor self, *, Tensor(a!) mantissa, Tensor(b!) exponent) -> (Tensor(a!) mantissa, Tensor(b!) exponent)", "dispatch": "True", "default": "False"}
at::Tensor frobenius_norm(const at::Tensor & self, at::IntArrayRef dim, bool keepdim); // {"schema": "aten::frobenius_norm.dim(Tensor self, int[1] dim, bool keepdim=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & frobenius_norm_out(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out); // {"schema": "aten::frobenius_norm.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor nuclear_norm(const at::Tensor & self, bool keepdim); // {"schema": "aten::nuclear_norm(Tensor self, bool keepdim=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & nuclear_norm_out(const at::Tensor & self, bool keepdim, at::Tensor & out); // {"schema": "aten::nuclear_norm.out(Tensor self, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor nuclear_norm(const at::Tensor & self, at::IntArrayRef dim, bool keepdim); // {"schema": "aten::nuclear_norm.dim(Tensor self, int[2] dim, bool keepdim=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & nuclear_norm_out(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out); // {"schema": "aten::nuclear_norm.dim_out(Tensor self, int[2] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor clone(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor positive(const at::Tensor & self); // {"schema": "aten::positive(Tensor(a) self) -> Tensor(a)", "dispatch": "False", "default": "True"}
const at::Tensor & resize_as_(const at::Tensor & self, const at::Tensor & the_template, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::resize_as_(Tensor(a!) self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor(a!)", "dispatch": "True", "default": "True"}
const at::Tensor & resize_as_sparse_(const at::Tensor & self, const at::Tensor & the_template); // {"schema": "aten::resize_as_sparse_(Tensor(a!) self, Tensor the_template) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & zero_(at::Tensor & self); // {"schema": "aten::zero_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & sub_out(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor sub(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha); // {"schema": "aten::sub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & sub_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha); // {"schema": "aten::sub_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor sub(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha); // {"schema": "aten::sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & sub_(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha); // {"schema": "aten::sub_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & subtract_out(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::subtract.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor subtract(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha); // {"schema": "aten::subtract.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & subtract_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha); // {"schema": "aten::subtract_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor subtract(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha); // {"schema": "aten::subtract.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & subtract_(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha); // {"schema": "aten::subtract_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor rsub(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha); // {"schema": "aten::rsub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & heaviside_out(const at::Tensor & self, const at::Tensor & values, at::Tensor & out); // {"schema": "aten::heaviside.out(Tensor self, Tensor values, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor heaviside(const at::Tensor & self, const at::Tensor & values); // {"schema": "aten::heaviside(Tensor self, Tensor values) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & heaviside_(at::Tensor & self, const at::Tensor & values); // {"schema": "aten::heaviside_(Tensor(a!) self, Tensor values) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor rsub(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha); // {"schema": "aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _sparse_addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha); // {"schema": "aten::_sparse_addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & sparse_sampled_addmm_out(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::sparse_sampled_addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor sparse_sampled_addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha); // {"schema": "aten::sparse_sampled_addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _sparse_mm_reduce_impl(const at::Tensor & self, const at::Tensor & other, c10::string_view reduce); // {"schema": "aten::_sparse_mm_reduce_impl(Tensor self, Tensor other, str reduce) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _sparse_mm_reduce_impl_backward(const at::Tensor & self, const at::Tensor & grad_out, const at::Tensor & weight, c10::string_view reduce, const at::Tensor & arg_out, ::std::array<bool,2> output_mask); // {"schema": "aten::_sparse_mm_reduce_impl_backward(Tensor self, Tensor grad_out, Tensor weight, str reduce, Tensor arg_out, bool[2] output_mask) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor & addmm_out(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha); // {"schema": "aten::addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::ScalarType out_dtype, const at::Scalar & beta, const at::Scalar & alpha); // {"schema": "aten::addmm.dtype(Tensor self, Tensor mat1, Tensor mat2, ScalarType out_dtype, *, Scalar beta=1, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & addmm_out(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, at::ScalarType out_dtype, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::addmm.dtype_out(Tensor self, Tensor mat1, Tensor mat2, ScalarType out_dtype, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & addmm_(at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha); // {"schema": "aten::addmm_(Tensor(a!) self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _addmm_activation_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 & out); // {"schema": "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!)", "dispatch": "True", "default": "False"}
at::Tensor _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); // {"schema": "aten::_addmm_activation(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, bool use_gelu=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _scaled_mm(const at::Tensor & self, const at::Tensor & mat2, const at::Tensor & scale_a, const at::Tensor & scale_b, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & scale_result, ::std::optional<at::ScalarType> out_dtype, bool use_fast_accum); // {"schema": "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", "dispatch": "True", "default": "False"}
at::Tensor & _scaled_mm_out(const at::Tensor & self, const at::Tensor & mat2, const at::Tensor & scale_a, const at::Tensor & scale_b, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & scale_result, ::std::optional<at::ScalarType> out_dtype, bool use_fast_accum, at::Tensor & out); // {"schema": "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!)", "dispatch": "True", "default": "False"}
at::Tensor _scaled_mm_v2(const at::Tensor & self, const at::Tensor & mat2, at::TensorList scale_a, at::IntArrayRef recipe_a, at::IntArrayRef swizzle_a, at::TensorList scale_b, at::IntArrayRef recipe_b, at::IntArrayRef swizzle_b, const ::std::optional<at::Tensor> & bias, ::std::optional<at::ScalarType> out_dtype, at::IntArrayRef contraction_dim, bool use_fast_accum); // {"schema": "aten::_scaled_mm_v2(Tensor self, Tensor mat2, Tensor[] scale_a, int[] recipe_a, int[] swizzle_a, Tensor[] scale_b, int[] recipe_b, int[] swizzle_b, Tensor? bias, ScalarType? out_dtype, int[] contraction_dim=[], bool use_fast_accum=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & _scaled_mm_v2_out(const at::Tensor & self, const at::Tensor & mat2, at::TensorList scale_a, at::IntArrayRef recipe_a, at::IntArrayRef swizzle_a, at::TensorList scale_b, at::IntArrayRef recipe_b, at::IntArrayRef swizzle_b, const ::std::optional<at::Tensor> & bias, ::std::optional<at::ScalarType> out_dtype, at::IntArrayRef contraction_dim, bool use_fast_accum, at::Tensor & out); // {"schema": "aten::_scaled_mm_v2.out(Tensor self, Tensor mat2, Tensor[] scale_a, int[] recipe_a, int[] swizzle_a, Tensor[] scale_b, int[] recipe_b, int[] swizzle_b, Tensor? bias, ScalarType? out_dtype, int[] contraction_dim=[], bool use_fast_accum=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _scaled_grouped_mm(const at::Tensor & self, const at::Tensor & mat2, const at::Tensor & scale_a, const at::Tensor & scale_b, const ::std::optional<at::Tensor> & offs, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & scale_result, ::std::optional<at::ScalarType> out_dtype, bool use_fast_accum); // {"schema": "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", "dispatch": "True", "default": "False"}
at::Tensor _scaled_grouped_mm_v2(const at::Tensor & self, const at::Tensor & mat2, at::TensorList scale_a, at::IntArrayRef recipe_a, at::IntArrayRef swizzle_a, at::TensorList scale_b, at::IntArrayRef recipe_b, at::IntArrayRef swizzle_b, const ::std::optional<at::Tensor> & offs, const ::std::optional<at::Tensor> & bias, ::std::optional<at::ScalarType> out_dtype, at::IntArrayRef contraction_dim, bool use_fast_accum); // {"schema": "aten::_scaled_grouped_mm_v2(Tensor self, Tensor mat2, Tensor[] scale_a, int[] recipe_a, int[] swizzle_a, Tensor[] scale_b, int[] recipe_b, int[] swizzle_b, Tensor? offs=None, Tensor? bias=None, ScalarType? out_dtype=None, int[] contraction_dim=[], bool use_fast_accum=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _grouped_mm(const at::Tensor & self, const at::Tensor & mat2, const ::std::optional<at::Tensor> & offs, const ::std::optional<at::Tensor> & bias, ::std::optional<at::ScalarType> out_dtype); // {"schema": "aten::_grouped_mm(Tensor self, Tensor mat2, Tensor? offs=None, Tensor? bias=None, ScalarType? out_dtype=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _sparse_compressed_tensor_with_dims(int64_t nnz, int64_t dense_dim, at::IntArrayRef size, at::IntArrayRef blocksize, at::ScalarType index_dtype, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::_sparse_compressed_tensor_with_dims(int nnz, int dense_dim, int[] size, int[] blocksize, ScalarType index_dtype, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor sparse_compressed_tensor(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::sparse_compressed_tensor.comp_plain_value_size(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor sparse_csr_tensor(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::sparse_csr_tensor.crow_col_value_size(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor sparse_csc_tensor(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::sparse_csc_tensor.ccol_row_value_size(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor sparse_bsr_tensor(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::sparse_bsr_tensor.crow_col_value_size(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor sparse_bsc_tensor(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::sparse_bsc_tensor.ccol_row_value_size(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor sparse_compressed_tensor(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::sparse_compressed_tensor.comp_plain_value(Tensor compressed_indices, Tensor plain_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor sparse_csr_tensor(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::sparse_csr_tensor.crow_col_value(Tensor crow_indices, Tensor col_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor sparse_csc_tensor(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::sparse_csc_tensor.ccol_row_value(Tensor ccol_indices, Tensor row_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor sparse_bsr_tensor(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::sparse_bsr_tensor.crow_col_value(Tensor crow_indices, Tensor col_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor sparse_bsc_tensor(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::sparse_bsc_tensor.ccol_row_value(Tensor ccol_indices, Tensor row_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _sparse_compressed_tensor_unsafe(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::_sparse_compressed_tensor_unsafe(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _sparse_csr_tensor_unsafe(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::_sparse_csr_tensor_unsafe(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _sparse_csc_tensor_unsafe(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::_sparse_csc_tensor_unsafe(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _sparse_bsr_tensor_unsafe(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::_sparse_bsr_tensor_unsafe(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _sparse_bsc_tensor_unsafe(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::_sparse_bsc_tensor_unsafe(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor sparse_coo_tensor(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::sparse_coo_tensor.size(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor sparse_coo_tensor(const at::Tensor & indices, const at::Tensor & values, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<bool> is_coalesced); // {"schema": "aten::sparse_coo_tensor.indices(Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor sparse_coo_tensor(const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<bool> is_coalesced); // {"schema": "aten::sparse_coo_tensor.indices_size(Tensor indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _sparse_coo_tensor_unsafe(const at::Tensor & indices, const at::Tensor & values, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<bool> is_coalesced); // {"schema": "aten::_sparse_coo_tensor_unsafe(Tensor indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor", "dispatch": "False", "default": "True"}
void _validate_sparse_coo_tensor_args(const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<bool> is_coalesced, ::std::optional<bool> check_pinning); // {"schema": "aten::_validate_sparse_coo_tensor_args(Tensor indices, Tensor values, int[] size, bool? is_coalesced=None, bool? check_pinning=None) -> ()", "dispatch": "False", "default": "True"}
void _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, ::std::optional<bool> check_pinning); // {"schema": "aten::_validate_sparse_compressed_tensor_args(Tensor compressed_indices, Tensor plain_indices, Tensor values, int[] size, Layout layout, bool? check_pinning=None) -> ()", "dispatch": "False", "default": "True"}
void _validate_sparse_csr_tensor_args(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<bool> check_pinning); // {"schema": "aten::_validate_sparse_csr_tensor_args(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, bool? check_pinning=None) -> ()", "dispatch": "False", "default": "True"}
void _validate_sparse_csc_tensor_args(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<bool> check_pinning); // {"schema": "aten::_validate_sparse_csc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, bool? check_pinning=None) -> ()", "dispatch": "False", "default": "True"}
void _validate_sparse_bsr_tensor_args(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<bool> check_pinning); // {"schema": "aten::_validate_sparse_bsr_tensor_args(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, bool? check_pinning=None) -> ()", "dispatch": "False", "default": "True"}
void _validate_sparse_bsc_tensor_args(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<bool> check_pinning); // {"schema": "aten::_validate_sparse_bsc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, bool? check_pinning=None) -> ()", "dispatch": "False", "default": "True"}
at::Tensor _sparse_coo_tensor_with_dims(int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::_sparse_coo_tensor_with_dims(int sparse_dim, int dense_dim, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _sparse_coo_tensor_with_dims_and_tensors(int64_t sparse_dim, int64_t dense_dim, c10::SymIntArrayRef size, const at::Tensor & indices, const at::Tensor & values, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<bool> is_coalesced); // {"schema": "aten::_sparse_coo_tensor_with_dims_and_tensors(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? is_coalesced=None) -> Tensor", "dispatch": "True", "default": "False"}
const at::Tensor & sparse_resize_(const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim); // {"schema": "aten::sparse_resize_(Tensor(a!) self, int[] size, int sparse_dim, int dense_dim) -> Tensor(a!)", "dispatch": "True", "default": "False"}
const at::Tensor & sparse_resize_and_clear_(const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim); // {"schema": "aten::sparse_resize_and_clear_(Tensor(a!) self, int[] size, int sparse_dim, int dense_dim) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor sparse_mask(const at::Tensor & self, const at::Tensor & mask); // {"schema": "aten::sparse_mask(Tensor self, Tensor mask) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _sparse_mask_projection(const at::Tensor & self, const at::Tensor & mask, bool accumulate_matches); // {"schema": "aten::_sparse_mask_projection(Tensor self, Tensor mask, bool accumulate_matches=False) -> Tensor", "dispatch": "True", "default": "False"}
::std::vector<at::Tensor> _to_cpu(at::TensorList tensors); // {"schema": "aten::_to_cpu(Tensor[] tensors) -> Tensor[]", "dispatch": "False", "default": "True"}
at::Tensor to_dense(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<bool> masked_grad); // {"schema": "aten::to_dense(Tensor self, ScalarType? dtype=None, *, bool? masked_grad=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _to_dense(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<bool> masked_grad); // {"schema": "aten::_to_dense(Tensor self, ScalarType? dtype=None, bool? masked_grad=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor to_dense_backward(const at::Tensor & grad, const at::Tensor & input, ::std::optional<bool> masked_grad); // {"schema": "aten::to_dense_backward(Tensor grad, Tensor input, bool? masked_grad=None) -> Tensor", "dispatch": "False", "default": "True"}
int64_t sparse_dim(const at::Tensor & self); // {"schema": "aten::sparse_dim(Tensor self) -> int", "dispatch": "True", "default": "True"}
int64_t _dimI(const at::Tensor & self); // {"schema": "aten::_dimI(Tensor self) -> int", "dispatch": "True", "default": "False"}
int64_t dense_dim(const at::Tensor & self); // {"schema": "aten::dense_dim(Tensor self) -> int", "dispatch": "True", "default": "True"}
int64_t _dimV(const at::Tensor & self); // {"schema": "aten::_dimV(Tensor self) -> int", "dispatch": "True", "default": "False"}
int64_t _nnz(const at::Tensor & self); // {"schema": "aten::_nnz(Tensor self) -> int", "dispatch": "True", "default": "False"}
at::Tensor coalesce(const at::Tensor & self); // {"schema": "aten::coalesce(Tensor(a) self) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor _coalesce(const at::Tensor & self); // {"schema": "aten::_coalesce(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
bool is_coalesced(const at::Tensor & self); // {"schema": "aten::is_coalesced(Tensor self) -> bool", "dispatch": "True", "default": "True"}
at::Tensor _indices(const at::Tensor & self); // {"schema": "aten::_indices(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "False"}
at::Tensor _values(const at::Tensor & self); // {"schema": "aten::_values(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "False"}
at::Tensor & _coalesced_(at::Tensor & self, bool coalesced); // {"schema": "aten::_coalesced_(Tensor(a!) self, bool coalesced) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor indices(const at::Tensor & self); // {"schema": "aten::indices(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor values(const at::Tensor & self); // {"schema": "aten::values(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor crow_indices(const at::Tensor & self); // {"schema": "aten::crow_indices(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor col_indices(const at::Tensor & self); // {"schema": "aten::col_indices(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor ccol_indices(const at::Tensor & self); // {"schema": "aten::ccol_indices(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor row_indices(const at::Tensor & self); // {"schema": "aten::row_indices(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor & hspmm_out(const at::Tensor & mat1, const at::Tensor & mat2, at::Tensor & out); // {"schema": "aten::hspmm.out(Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor hspmm(const at::Tensor & mat1, const at::Tensor & mat2); // {"schema": "aten::hspmm(Tensor mat1, Tensor mat2) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & copy_sparse_to_sparse_(at::Tensor & self, const at::Tensor & src, bool non_blocking); // {"schema": "aten::copy_sparse_to_sparse_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> Tensor(a!)", "dispatch": "True", "default": "False"}
::std::vector<at::Tensor> unbind(const at::Tensor & self, int64_t dim); // {"schema": "aten::unbind.int(Tensor(a -> *) self, int dim=0) -> Tensor(a)[]", "dispatch": "True", "default": "True"}
at::Tensor to_sparse(const at::Tensor & self, int64_t sparse_dim); // {"schema": "aten::to_sparse.sparse_dim(Tensor self, int sparse_dim) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _to_sparse(const at::Tensor & self, int64_t sparse_dim); // {"schema": "aten::_to_sparse.sparse_dim(Tensor self, int sparse_dim) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor to_sparse(const at::Tensor & self, ::std::optional<at::Layout> layout, at::OptionalIntArrayRef blocksize, ::std::optional<int64_t> dense_dim); // {"schema": "aten::to_sparse(Tensor self, *, Layout? layout=None, int[2]? blocksize=None, int? dense_dim=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _to_sparse(const at::Tensor & self, ::std::optional<at::Layout> layout, at::OptionalIntArrayRef blocksize, ::std::optional<int64_t> dense_dim); // {"schema": "aten::_to_sparse(Tensor self, *, Layout? layout=None, int[2]? blocksize=None, int? dense_dim=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor to_sparse_csr(const at::Tensor & self, ::std::optional<int64_t> dense_dim); // {"schema": "aten::to_sparse_csr(Tensor self, int? dense_dim=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _to_sparse_csr(const at::Tensor & self, ::std::optional<int64_t> dense_dim); // {"schema": "aten::_to_sparse_csr(Tensor self, int? dense_dim=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor to_sparse_csc(const at::Tensor & self, ::std::optional<int64_t> dense_dim); // {"schema": "aten::to_sparse_csc(Tensor self, int? dense_dim=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _to_sparse_csc(const at::Tensor & self, ::std::optional<int64_t> dense_dim); // {"schema": "aten::_to_sparse_csc(Tensor self, int? dense_dim=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor to_sparse_bsr(const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim); // {"schema": "aten::to_sparse_bsr(Tensor self, int[2] blocksize, int? dense_dim=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _to_sparse_bsr(const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim); // {"schema": "aten::_to_sparse_bsr(Tensor self, int[2] blocksize, int? dense_dim=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor to_sparse_bsc(const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim); // {"schema": "aten::to_sparse_bsc(Tensor self, int[2] blocksize, int? dense_dim=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _to_sparse_bsc(const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim); // {"schema": "aten::_to_sparse_bsc(Tensor self, int[2] blocksize, int? dense_dim=None) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _to_sparse_semi_structured(const at::Tensor & dense); // {"schema": "aten::_to_sparse_semi_structured(Tensor dense) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor to_mkldnn(const at::Tensor & self, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::to_mkldnn(Tensor self, ScalarType? dtype=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor mkldnn_reorder_conv2d_weight(const at::Tensor & self, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::OptionalSymIntArrayRef input_size); // {"schema": "aten::mkldnn_reorder_conv2d_weight(Tensor self, SymInt[2] padding=0, SymInt[2] stride=1, SymInt[2] dilation=1, SymInt groups=1, SymInt[]? input_size=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor mkldnn_reorder_conv3d_weight(const at::Tensor & self, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::OptionalSymIntArrayRef input_size); // {"schema": "aten::mkldnn_reorder_conv3d_weight(Tensor self, SymInt[3] padding=0, SymInt[3] stride=1, SymInt[3] dilation=1, SymInt groups=1, SymInt[]? input_size=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor to_mkldnn_backward(const at::Tensor & grad, const at::Tensor & input); // {"schema": "aten::to_mkldnn_backward(Tensor grad, Tensor input) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor quantize_per_tensor_dynamic(const at::Tensor & self, at::ScalarType dtype, bool reduce_range); // {"schema": "aten::quantize_per_tensor_dynamic(Tensor self, ScalarType dtype, bool reduce_range) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor quantize_per_tensor(const at::Tensor & self, double scale, int64_t zero_point, at::ScalarType dtype); // {"schema": "aten::quantize_per_tensor(Tensor self, float scale, int zero_point, ScalarType dtype) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor quantize_per_tensor(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, at::ScalarType dtype); // {"schema": "aten::quantize_per_tensor.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype) -> Tensor", "dispatch": "True", "default": "False"}
::std::vector<at::Tensor> quantize_per_tensor(at::TensorList tensors, const at::Tensor & scales, const at::Tensor & zero_points, at::ScalarType dtype); // {"schema": "aten::quantize_per_tensor.tensors(Tensor[] tensors, Tensor scales, Tensor zero_points, ScalarType dtype) -> Tensor[]", "dispatch": "True", "default": "False"}
at::Tensor quantize_per_channel(const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype); // {"schema": "aten::quantize_per_channel(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor dequantize(const at::Tensor & self); // {"schema": "aten::dequantize.self(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
::std::vector<at::Tensor> dequantize(at::TensorList tensors); // {"schema": "aten::dequantize.tensors(Tensor[] tensors) -> Tensor[]", "dispatch": "True", "default": "False"}
double q_scale(const at::Tensor & self); // {"schema": "aten::q_scale(Tensor self) -> float", "dispatch": "True", "default": "False"}
int64_t q_zero_point(const at::Tensor & self); // {"schema": "aten::q_zero_point(Tensor self) -> int", "dispatch": "True", "default": "False"}
at::Tensor q_per_channel_scales(const at::Tensor & self); // {"schema": "aten::q_per_channel_scales(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor q_per_channel_zero_points(const at::Tensor & self); // {"schema": "aten::q_per_channel_zero_points(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
int64_t q_per_channel_axis(const at::Tensor & self); // {"schema": "aten::q_per_channel_axis(Tensor self) -> int", "dispatch": "True", "default": "False"}
at::Tensor int_repr(const at::Tensor & self); // {"schema": "aten::int_repr(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _make_per_tensor_quantized_tensor(const at::Tensor & self, double scale, int64_t zero_point); // {"schema": "aten::_make_per_tensor_quantized_tensor(Tensor self, float scale, int zero_point) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _make_per_channel_quantized_tensor(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis); // {"schema": "aten::_make_per_channel_quantized_tensor(Tensor self, Tensor scale, Tensor zero_point, int axis) -> Tensor", "dispatch": "True", "default": "False"}
at::QScheme qscheme(const at::Tensor & self); // {"schema": "aten::qscheme(Tensor self) -> QScheme", "dispatch": "True", "default": "False"}
at::Tensor fake_quantize_per_tensor_affine(const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max); // {"schema": "aten::fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor 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); // {"schema": "aten::fake_quantize_per_tensor_affine.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> fake_quantize_per_tensor_affine_cachemask(const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max); // {"schema": "aten::fake_quantize_per_tensor_affine_cachemask(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> (Tensor output, Tensor mask)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _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); // {"schema": "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)", "dispatch": "True", "default": "False"}
at::Tensor fake_quantize_per_tensor_affine_cachemask_backward(const at::Tensor & grad, const at::Tensor & mask); // {"schema": "aten::fake_quantize_per_tensor_affine_cachemask_backward(Tensor grad, Tensor mask) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _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); // {"schema": "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", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _fake_quantize_learnable_per_tensor_affine_backward(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor); // {"schema": "aten::_fake_quantize_learnable_per_tensor_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor 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); // {"schema": "aten::fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> fake_quantize_per_channel_affine_cachemask(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max); // {"schema": "aten::fake_quantize_per_channel_affine_cachemask(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> (Tensor output, Tensor mask)", "dispatch": "True", "default": "False"}
at::Tensor fake_quantize_per_channel_affine_cachemask_backward(const at::Tensor & grad, const at::Tensor & mask); // {"schema": "aten::fake_quantize_per_channel_affine_cachemask_backward(Tensor grad, Tensor mask) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _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); // {"schema": "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", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _fake_quantize_learnable_per_channel_affine_backward(const at::Tensor & grad, 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); // {"schema": "aten::_fake_quantize_learnable_per_channel_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor 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); // {"schema": "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", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> _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); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<double,int64_t> _choose_qparams_per_tensor(const at::Tensor & self, bool reduce_range); // {"schema": "aten::_choose_qparams_per_tensor(Tensor self, bool reduce_range=False) -> (float, int)", "dispatch": "False", "default": "True"}
at::Tensor _saturate_weight_to_fp16(const at::Tensor & weight); // {"schema": "aten::_saturate_weight_to_fp16(Tensor weight) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> choose_qparams_optimized(const at::Tensor & input, int64_t numel, int64_t n_bins, double ratio, int64_t bit_width); // {"schema": "aten::choose_qparams_optimized(Tensor input, int numel, int n_bins, float ratio, int bit_width) -> (Tensor, Tensor)", "dispatch": "False", "default": "True"}
at::Tensor _autocast_to_reduced_precision(const at::Tensor & self, bool cuda_enabled, bool cpu_enabled, at::ScalarType cuda_dtype, at::ScalarType cpu_dtype); // {"schema": "aten::_autocast_to_reduced_precision(Tensor(a) self, bool cuda_enabled, bool cpu_enabled, ScalarType cuda_dtype, ScalarType cpu_dtype) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor _autocast_to_full_precision(const at::Tensor & self, bool cuda_enabled, bool cpu_enabled); // {"schema": "aten::_autocast_to_full_precision(Tensor(a) self, bool cuda_enabled, bool cpu_enabled) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor _to_copy(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, bool non_blocking, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::_to_copy(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor to(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, bool non_blocking, bool copy, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::to.dtype_layout(Tensor(a) self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor to(const at::Tensor & self, at::Device device, at::ScalarType dtype, bool non_blocking, bool copy, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::to.device(Tensor(a) self, Device device, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor to(const at::Tensor & self, at::ScalarType dtype, bool non_blocking, bool copy, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::to.dtype(Tensor(a) self, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor to(const at::Tensor & self, const at::Tensor & other, bool non_blocking, bool copy, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::to.other(Tensor(a) self, Tensor other, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a)", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> meshgrid(at::TensorList tensors); // {"schema": "aten::meshgrid(Tensor[] tensors) -> Tensor[]", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> meshgrid(at::TensorList tensors, c10::string_view indexing); // {"schema": "aten::meshgrid.indexing(Tensor[] tensors, *, str indexing) -> Tensor[]", "dispatch": "False", "default": "True"}
at::Tensor cartesian_prod(at::TensorList tensors); // {"schema": "aten::cartesian_prod(Tensor[] tensors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor combinations(const at::Tensor & self, int64_t r, bool with_replacement); // {"schema": "aten::combinations(Tensor self, int r=2, bool with_replacement=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Scalar item(const at::Tensor & self); // {"schema": "aten::item(Tensor self) -> Scalar", "dispatch": "False", "default": "True"}
at::ScalarType result_type(const at::Tensor & tensor, const at::Tensor & other); // {"schema": "aten::result_type.Tensor(Tensor tensor, Tensor other) -> ScalarType", "dispatch": "False", "default": "True"}
at::ScalarType result_type(const at::Tensor & tensor, const at::Scalar & other); // {"schema": "aten::result_type.Scalar(Tensor tensor, Scalar other) -> ScalarType", "dispatch": "False", "default": "True"}
at::ScalarType result_type(const at::Scalar & scalar, const at::Tensor & tensor); // {"schema": "aten::result_type.Scalar_Tensor(Scalar scalar, Tensor tensor) -> ScalarType", "dispatch": "False", "default": "True"}
at::ScalarType result_type(const at::Scalar & scalar1, const at::Scalar & scalar2); // {"schema": "aten::result_type.Scalar_Scalar(Scalar scalar1, Scalar scalar2) -> ScalarType", "dispatch": "False", "default": "True"}
bool can_cast(at::ScalarType from_, at::ScalarType to); // {"schema": "aten::can_cast(ScalarType from_, ScalarType to) -> bool", "dispatch": "False", "default": "True"}
at::ScalarType promote_types(at::ScalarType type1, at::ScalarType type2); // {"schema": "aten::promote_types(ScalarType type1, ScalarType type2) -> ScalarType", "dispatch": "False", "default": "True"}
at::Scalar _local_scalar_dense(const at::Tensor & self); // {"schema": "aten::_local_scalar_dense(Tensor self) -> Scalar", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _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); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,::std::vector<at::Tensor>,::std::vector<at::Tensor>> lstm_mps_backward(const ::std::optional<at::Tensor> & grad_y, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, const at::Tensor & layersOutputs, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); // {"schema": "aten::lstm_mps_backward(Tensor? grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor layersOutputs, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor[], Tensor[])", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_fused_lstm_cell(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & cx, const ::std::optional<at::Tensor> & input_bias, const ::std::optional<at::Tensor> & hidden_bias); // {"schema": "aten::_thnn_fused_lstm_cell(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_fused_lstm_cell_backward_impl(const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias); // {"schema": "aten::_thnn_fused_lstm_cell_backward_impl(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _thnn_fused_lstm_cell_backward(const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias); // {"schema": "aten::_thnn_fused_lstm_cell_backward(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor, Tensor, Tensor)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _thnn_differentiable_lstm_cell_backward(const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const ::std::optional<at::Tensor> & input_bias, const ::std::optional<at::Tensor> & hidden_bias, const at::Tensor & cx, const at::Tensor & cy); // {"schema": "aten::_thnn_differentiable_lstm_cell_backward(Tensor? grad_hy, Tensor? grad_cy, Tensor input_gates, Tensor hidden_gates, Tensor? input_bias, Tensor? hidden_bias, Tensor cx, Tensor cy) -> (Tensor, Tensor, Tensor, Tensor, Tensor)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> _thnn_fused_gru_cell(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const ::std::optional<at::Tensor> & input_bias, const ::std::optional<at::Tensor> & hidden_bias); // {"schema": "aten::_thnn_fused_gru_cell(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _thnn_fused_gru_cell_backward(const at::Tensor & grad_hy, const at::Tensor & workspace, bool has_bias); // {"schema": "aten::_thnn_fused_gru_cell_backward(Tensor grad_hy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _thnn_differentiable_gru_cell_backward(const at::Tensor & grad_hy, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const ::std::optional<at::Tensor> & input_bias, const ::std::optional<at::Tensor> & hidden_bias); // {"schema": "aten::_thnn_differentiable_gru_cell_backward(Tensor grad_hy, Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias, Tensor? hidden_bias) -> (Tensor, Tensor, Tensor, Tensor, Tensor)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> lstm(const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); // {"schema": "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)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> lstm(const at::Tensor & data, const at::Tensor & batch_sizes, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); // {"schema": "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)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> gru(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); // {"schema": "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)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> gru(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); // {"schema": "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)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> rnn_tanh(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); // {"schema": "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)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> rnn_tanh(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); // {"schema": "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)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> rnn_relu(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); // {"schema": "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)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> rnn_relu(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); // {"schema": "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)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> lstm_cell(const at::Tensor & input, at::TensorList hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const ::std::optional<at::Tensor> & b_ih, const ::std::optional<at::Tensor> & b_hh); // {"schema": "aten::lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> (Tensor, Tensor)", "dispatch": "False", "default": "True"}
at::Tensor gru_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const ::std::optional<at::Tensor> & b_ih, const ::std::optional<at::Tensor> & b_hh); // {"schema": "aten::gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor rnn_tanh_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const ::std::optional<at::Tensor> & b_ih, const ::std::optional<at::Tensor> & b_hh); // {"schema": "aten::rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor rnn_relu_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const ::std::optional<at::Tensor> & b_ih, const ::std::optional<at::Tensor> & b_hh); // {"schema": "aten::rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> quantized_lstm_cell(const at::Tensor & input, at::TensorList hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh); // {"schema": "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)", "dispatch": "False", "default": "True"}
at::Tensor quantized_gru_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor quantized_rnn_relu_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh); // {"schema": "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", "dispatch": "False", "default": "True"}
at::Tensor quantized_rnn_tanh_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh); // {"schema": "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", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> _pack_padded_sequence(const at::Tensor & input, const at::Tensor & lengths, bool batch_first); // {"schema": "aten::_pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first) -> (Tensor, Tensor)", "dispatch": "True", "default": "True"}
at::Tensor _pack_padded_sequence_backward(const at::Tensor & grad, c10::SymIntArrayRef input_size, const at::Tensor & batch_sizes, bool batch_first); // {"schema": "aten::_pack_padded_sequence_backward(Tensor grad, SymInt[] input_size, Tensor batch_sizes, bool batch_first) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> _pad_packed_sequence(const at::Tensor & data, const at::Tensor & batch_sizes, bool batch_first, const at::Scalar & padding_value, int64_t total_length); // {"schema": "aten::_pad_packed_sequence(Tensor data, Tensor batch_sizes, bool batch_first, Scalar padding_value, int total_length) -> (Tensor, Tensor)", "dispatch": "False", "default": "True"}
at::Tensor & set_(at::Tensor & self, at::Storage source); // {"schema": "aten::set_.source_Storage(Tensor(a!) self, Storage source) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & set_(at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride); // {"schema": "aten::set_.source_Storage_storage_offset(Tensor(a!) self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & set_(at::Tensor & self, const at::Tensor & source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride); // {"schema": "aten::set_.source_Tensor_storage_offset(Tensor(a!) self, Tensor source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & set_(at::Tensor & self, const at::Tensor & source); // {"schema": "aten::set_.source_Tensor(Tensor(a!) self, Tensor source) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & set_(at::Tensor & self); // {"schema": "aten::set_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor lift(const at::Tensor & self); // {"schema": "aten::lift(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor lift_fresh(const at::Tensor & self); // {"schema": "aten::lift_fresh(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor lift_fresh_copy(const at::Tensor & self); // {"schema": "aten::lift_fresh_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
bool is_set_to(const at::Tensor & self, const at::Tensor & tensor); // {"schema": "aten::is_set_to(Tensor self, Tensor tensor) -> bool", "dispatch": "True", "default": "False"}
at::Tensor & masked_fill_(at::Tensor & self, const at::Tensor & mask, const at::Scalar & value); // {"schema": "aten::masked_fill_.Scalar(Tensor(a!) self, Tensor mask, Scalar value) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor masked_fill(const at::Tensor & self, const at::Tensor & mask, const at::Scalar & value); // {"schema": "aten::masked_fill.Scalar(Tensor self, Tensor mask, Scalar value) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & masked_fill_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & value); // {"schema": "aten::masked_fill_.Tensor(Tensor(a!) self, Tensor mask, Tensor value) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor masked_fill(const at::Tensor & self, const at::Tensor & mask, const at::Tensor & value); // {"schema": "aten::masked_fill.Tensor(Tensor self, Tensor mask, Tensor value) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & masked_scatter_(at::Tensor & self, const at::Tensor & mask, const at::Tensor & source); // {"schema": "aten::masked_scatter_(Tensor(a!) self, Tensor mask, Tensor source) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor masked_scatter(const at::Tensor & self, const at::Tensor & mask, const at::Tensor & source); // {"schema": "aten::masked_scatter(Tensor self, Tensor mask, Tensor source) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor masked_scatter_backward(const at::Tensor & grad_output, const at::Tensor & mask, c10::SymIntArrayRef sizes); // {"schema": "aten::masked_scatter_backward(Tensor grad_output, Tensor mask, SymInt[] sizes) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _masked_softmax(const at::Tensor & self, const at::Tensor & mask, ::std::optional<int64_t> dim, ::std::optional<int64_t> mask_type); // {"schema": "aten::_masked_softmax(Tensor self, Tensor mask, int? dim=None, int? mask_type=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _masked_softmax_backward(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & mask, ::std::optional<int64_t> dim); // {"schema": "aten::_masked_softmax_backward(Tensor grad_output, Tensor output, Tensor mask, int? dim=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor view(const at::Tensor & self, c10::SymIntArrayRef size); // {"schema": "aten::view(Tensor(a) self, SymInt[] size) -> Tensor(a)", "dispatch": "True", "default": "False"}
at::Tensor view(const at::Tensor & self, at::ScalarType dtype); // {"schema": "aten::view.dtype(Tensor(a) self, ScalarType dtype) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor & put_(at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate); // {"schema": "aten::put_(Tensor(a!) self, Tensor index, Tensor source, bool accumulate=False) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor put(const at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate); // {"schema": "aten::put(Tensor self, Tensor index, Tensor source, bool accumulate=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & index_add_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::index_add.out(Tensor self, int dim, Tensor index, Tensor source, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & index_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha); // {"schema": "aten::index_add_(Tensor(a!) self, int dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor index_add(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha); // {"schema": "aten::index_add(Tensor self, int dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & index_reduce_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 & out); // {"schema": "aten::index_reduce.out(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & index_reduce_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self); // {"schema": "aten::index_reduce_(Tensor(a!) self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor index_reduce(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self); // {"schema": "aten::index_reduce(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & index_fill_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value); // {"schema": "aten::index_fill_.int_Scalar(Tensor(a!) self, int dim, Tensor index, Scalar value) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor index_fill(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value); // {"schema": "aten::index_fill.int_Scalar(Tensor self, int dim, Tensor index, Scalar value) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & index_fill_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value); // {"schema": "aten::index_fill_.int_Tensor(Tensor(a!) self, int dim, Tensor index, Tensor value) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor index_fill(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value); // {"schema": "aten::index_fill.int_Tensor(Tensor self, int dim, Tensor index, Tensor value) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src); // {"schema": "aten::scatter.src(Tensor self, int dim, Tensor index, Tensor src) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src); // {"schema": "aten::scatter_.src(Tensor(a!) self, int dim, Tensor index, Tensor src) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & scatter_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, at::Tensor & out); // {"schema": "aten::scatter.src_out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value); // {"schema": "aten::scatter.value(Tensor self, int dim, Tensor index, Scalar value) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value); // {"schema": "aten::scatter_.value(Tensor(a!) self, int dim, Tensor index, Scalar value) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & scatter_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, at::Tensor & out); // {"schema": "aten::scatter.value_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce); // {"schema": "aten::scatter.reduce(Tensor self, int dim, Tensor index, Tensor src, *, str reduce) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce); // {"schema": "aten::scatter_.reduce(Tensor(a!) self, int dim, Tensor index, Tensor src, *, str reduce) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & scatter_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, at::Tensor & out); // {"schema": "aten::scatter.reduce_out(Tensor self, int dim, Tensor index, Tensor src, *, str reduce, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce); // {"schema": "aten::scatter.value_reduce(Tensor self, int dim, Tensor index, Scalar value, *, str reduce) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce); // {"schema": "aten::scatter_.value_reduce(Tensor(a!) self, int dim, Tensor index, Scalar value, *, str reduce) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & scatter_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce, at::Tensor & out); // {"schema": "aten::scatter.value_reduce_out(Tensor self, int dim, Tensor index, Scalar value, *, str reduce, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor scatter_add(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src); // {"schema": "aten::scatter_add(Tensor self, int dim, Tensor index, Tensor src) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & scatter_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src); // {"schema": "aten::scatter_add_(Tensor(a!) self, int dim, Tensor index, Tensor src) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & scatter_add_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, at::Tensor & out); // {"schema": "aten::scatter_add.out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor scatter_reduce(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self); // {"schema": "aten::scatter_reduce.two(Tensor self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & scatter_reduce_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self); // {"schema": "aten::scatter_reduce_.two(Tensor(a!) self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & scatter_reduce_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 & out); // {"schema": "aten::scatter_reduce.two_out(Tensor self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & eq_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::eq_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & eq_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::eq_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_and_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::bitwise_and.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & bitwise_and_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::bitwise_and.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor bitwise_and(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::bitwise_and.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor bitwise_and(const at::Scalar & self, const at::Tensor & other); // {"schema": "aten::bitwise_and.Scalar_Tensor(Scalar self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor bitwise_and(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::bitwise_and.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_and_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::bitwise_and_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_and_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::bitwise_and_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor __and__(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::__and__.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor __and__(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::__and__.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & __iand__(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::__iand__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & __iand__(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::__iand__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & bitwise_or_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::bitwise_or.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & bitwise_or_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::bitwise_or.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor bitwise_or(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::bitwise_or.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor bitwise_or(const at::Scalar & self, const at::Tensor & other); // {"schema": "aten::bitwise_or.Scalar_Tensor(Scalar self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor bitwise_or(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::bitwise_or.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_or_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::bitwise_or_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_or_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::bitwise_or_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor __or__(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::__or__.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor __or__(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::__or__.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & __ior__(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::__ior__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & __ior__(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::__ior__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & bitwise_xor_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::bitwise_xor.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & bitwise_xor_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::bitwise_xor.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor bitwise_xor(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::bitwise_xor.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor bitwise_xor(const at::Scalar & self, const at::Tensor & other); // {"schema": "aten::bitwise_xor.Scalar_Tensor(Scalar self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor bitwise_xor(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::bitwise_xor.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_xor_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::bitwise_xor_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_xor_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::bitwise_xor_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor __xor__(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::__xor__.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor __xor__(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::__xor__.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & __ixor__(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::__ixor__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & __ixor__(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::__ixor__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor __lshift__(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::__lshift__.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor __lshift__(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::__lshift__.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & __ilshift__(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::__ilshift__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & __ilshift__(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::__ilshift__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor bitwise_left_shift(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::bitwise_left_shift.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_left_shift_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::bitwise_left_shift_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_left_shift_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::bitwise_left_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor bitwise_left_shift(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::bitwise_left_shift.Tensor_Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_left_shift_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::bitwise_left_shift_.Tensor_Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_left_shift_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::bitwise_left_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor bitwise_left_shift(const at::Scalar & self, const at::Tensor & other); // {"schema": "aten::bitwise_left_shift.Scalar_Tensor(Scalar self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor __rshift__(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::__rshift__.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor __rshift__(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::__rshift__.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & __irshift__(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::__irshift__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & __irshift__(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::__irshift__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor bitwise_right_shift(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::bitwise_right_shift.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_right_shift_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::bitwise_right_shift_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_right_shift_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::bitwise_right_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor bitwise_right_shift(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::bitwise_right_shift.Tensor_Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_right_shift_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::bitwise_right_shift_.Tensor_Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_right_shift_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::bitwise_right_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor bitwise_right_shift(const at::Scalar & self, const at::Tensor & other); // {"schema": "aten::bitwise_right_shift.Scalar_Tensor(Scalar self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & tril_(at::Tensor & self, c10::SymInt diagonal); // {"schema": "aten::tril_(Tensor(a!) self, SymInt diagonal=0) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & triu_(at::Tensor & self, c10::SymInt diagonal); // {"schema": "aten::triu_(Tensor(a!) self, SymInt diagonal=0) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & digamma_(at::Tensor & self); // {"schema": "aten::digamma_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & lerp_(at::Tensor & self, const at::Tensor & end, const at::Scalar & weight); // {"schema": "aten::lerp_.Scalar(Tensor(a!) self, Tensor end, Scalar weight) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & lerp_(at::Tensor & self, const at::Tensor & end, const at::Tensor & weight); // {"schema": "aten::lerp_.Tensor(Tensor(a!) self, Tensor end, Tensor weight) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & addbmm_(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha); // {"schema": "aten::addbmm_(Tensor(a!) self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & addbmm_out(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::addbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor addbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha); // {"schema": "aten::addbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & random_(at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator); // {"schema": "aten::random_.from(Tensor(a!) self, int from, int? to, *, Generator? generator=None) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & random_(at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator); // {"schema": "aten::random_.to(Tensor(a!) self, int to, *, Generator? generator=None) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & random_(at::Tensor & self, ::std::optional<at::Generator> generator); // {"schema": "aten::random_(Tensor(a!) self, *, Generator? generator=None) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & uniform_(at::Tensor & self, double from, double to, ::std::optional<at::Generator> generator); // {"schema": "aten::uniform_(Tensor(a!) self, float from=0, float to=1, *, Generator? generator=None) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & cauchy_(at::Tensor & self, double median, double sigma, ::std::optional<at::Generator> generator); // {"schema": "aten::cauchy_(Tensor(a!) self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & log_normal_(at::Tensor & self, double mean, double std, ::std::optional<at::Generator> generator); // {"schema": "aten::log_normal_(Tensor(a!) self, float mean=1, float std=2, *, Generator? generator=None) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & exponential_(at::Tensor & self, double lambd, ::std::optional<at::Generator> generator); // {"schema": "aten::exponential_(Tensor(a!) self, float lambd=1, *, Generator? generator=None) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & geometric_(at::Tensor & self, double p, ::std::optional<at::Generator> generator); // {"schema": "aten::geometric_(Tensor(a!) self, float p, *, Generator? generator=None) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & diag_out(const at::Tensor & self, int64_t diagonal, at::Tensor & out); // {"schema": "aten::diag.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor diag(const at::Tensor & self, int64_t diagonal); // {"schema": "aten::diag(Tensor self, int diagonal=0) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & cross_out(const at::Tensor & self, const at::Tensor & other, ::std::optional<int64_t> dim, at::Tensor & out); // {"schema": "aten::cross.out(Tensor self, Tensor other, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor cross(const at::Tensor & self, const at::Tensor & other, ::std::optional<int64_t> dim); // {"schema": "aten::cross(Tensor self, Tensor other, int? dim=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & triu_out(const at::Tensor & self, c10::SymInt diagonal, at::Tensor & out); // {"schema": "aten::triu.out(Tensor self, SymInt diagonal=0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor triu(const at::Tensor & self, c10::SymInt diagonal); // {"schema": "aten::triu(Tensor self, SymInt diagonal=0) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & tril_out(const at::Tensor & self, c10::SymInt diagonal, at::Tensor & out); // {"schema": "aten::tril.out(Tensor self, SymInt diagonal=0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor tril(const at::Tensor & self, c10::SymInt diagonal); // {"schema": "aten::tril(Tensor self, SymInt diagonal=0) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor tril_indices(int64_t row, int64_t col, int64_t offset, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::tril_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor triu_indices(int64_t row, int64_t col, int64_t offset, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::triu_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor trace(const at::Tensor & self); // {"schema": "aten::trace(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor trace_backward(const at::Tensor & grad, c10::SymIntArrayRef sizes); // {"schema": "aten::trace_backward(Tensor grad, SymInt[] sizes) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & ne_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor ne(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::ne.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & ne_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor ne(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::ne.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & ne_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::ne_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & ne_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::ne_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & not_equal_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::not_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor not_equal(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::not_equal.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & not_equal_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::not_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor not_equal(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::not_equal.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & not_equal_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::not_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & not_equal_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::not_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & eq_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::eq.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor eq(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::eq.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & eq_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::eq.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor eq(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::eq.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & ge_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor ge(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::ge.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & ge_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor ge(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::ge.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & ge_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::ge_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & ge_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::ge_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & greater_equal_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::greater_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor greater_equal(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::greater_equal.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & greater_equal_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::greater_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor greater_equal(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::greater_equal.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & greater_equal_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::greater_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & greater_equal_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::greater_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & le_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::le.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor le(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::le.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & le_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::le.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor le(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::le.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & le_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::le_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & le_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::le_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & less_equal_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::less_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor less_equal(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::less_equal.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & less_equal_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::less_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor less_equal(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::less_equal.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & less_equal_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::less_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & less_equal_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::less_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & gt_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::gt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor gt(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::gt.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & gt_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::gt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor gt(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::gt.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & gt_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::gt_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & gt_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::gt_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & greater_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::greater.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor greater(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::greater.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & greater_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::greater.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor greater(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::greater.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & greater_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::greater_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & greater_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::greater_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & lt_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::lt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor lt(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::lt.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & lt_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::lt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor lt(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::lt.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & lt_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::lt_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & lt_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::lt_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & less_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::less.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor less(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::less.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & less_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::less.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor less(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::less.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & less_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::less_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & less_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::less_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & take_out(const at::Tensor & self, const at::Tensor & index, at::Tensor & out); // {"schema": "aten::take.out(Tensor self, Tensor index, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor take(const at::Tensor & self, const at::Tensor & index); // {"schema": "aten::take(Tensor self, Tensor index) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & take_along_dim_out(const at::Tensor & self, const at::Tensor & indices, ::std::optional<int64_t> dim, at::Tensor & out); // {"schema": "aten::take_along_dim.out(Tensor self, Tensor indices, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor take_along_dim(const at::Tensor & self, const at::Tensor & indices, ::std::optional<int64_t> dim); // {"schema": "aten::take_along_dim(Tensor self, Tensor indices, int? dim=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & index_select_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, at::Tensor & out); // {"schema": "aten::index_select.out(Tensor self, int dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor index_select(const at::Tensor & self, int64_t dim, const at::Tensor & index); // {"schema": "aten::index_select(Tensor self, int dim, Tensor index) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor index_select_backward(const at::Tensor & grad, c10::SymIntArrayRef self_sizes, int64_t dim, const at::Tensor & index); // {"schema": "aten::index_select_backward(Tensor grad, SymInt[] self_sizes, int dim, Tensor index) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & masked_select_out(const at::Tensor & self, const at::Tensor & mask, at::Tensor & out); // {"schema": "aten::masked_select.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor masked_select(const at::Tensor & self, const at::Tensor & mask); // {"schema": "aten::masked_select(Tensor self, Tensor mask) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor masked_select_backward(const at::Tensor & grad, const at::Tensor & input, const at::Tensor & mask); // {"schema": "aten::masked_select_backward(Tensor grad, Tensor input, Tensor mask) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & nonzero_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::nonzero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor nonzero(const at::Tensor & self); // {"schema": "aten::nonzero(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & nonzero_static_out(const at::Tensor & self, c10::SymInt size, int64_t fill_value, at::Tensor & out); // {"schema": "aten::nonzero_static.out(Tensor self, *, SymInt size, int fill_value=-1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor nonzero_static(const at::Tensor & self, c10::SymInt size, int64_t fill_value); // {"schema": "aten::nonzero_static(Tensor self, *, SymInt size, int fill_value=-1) -> Tensor", "dispatch": "True", "default": "False"}
::std::vector<at::Tensor> nonzero_numpy(const at::Tensor & self); // {"schema": "aten::nonzero_numpy(Tensor self) -> Tensor[]", "dispatch": "False", "default": "True"}
at::Tensor argwhere(const at::Tensor & self); // {"schema": "aten::argwhere(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & gather_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad, at::Tensor & out); // {"schema": "aten::gather.out(Tensor self, int dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor gather(const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad); // {"schema": "aten::gather(Tensor self, int dim, Tensor index, *, bool sparse_grad=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor gather_backward(const at::Tensor & grad, const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad); // {"schema": "aten::gather_backward(Tensor grad, Tensor self, int dim, Tensor index, bool sparse_grad) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _gather_sparse_backward(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & grad); // {"schema": "aten::_gather_sparse_backward(Tensor self, int dim, Tensor index, Tensor grad) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & addcmul_out(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value, at::Tensor & out); // {"schema": "aten::addcmul.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor addcmul(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value); // {"schema": "aten::addcmul(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & addcmul_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value); // {"schema": "aten::addcmul_(Tensor(a!) self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & addcdiv_out(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value, at::Tensor & out); // {"schema": "aten::addcdiv.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor addcdiv(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value); // {"schema": "aten::addcdiv(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & addcdiv_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value); // {"schema": "aten::addcdiv_(Tensor(a!) self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor cross_entropy_loss(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, double label_smoothing); // {"schema": "aten::cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, float label_smoothing=0.0) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_out(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> triangular_solve(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular); // {"schema": "aten::triangular_solve(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False) -> (Tensor solution, Tensor cloned_coefficient)", "dispatch": "True", "default": "True"}
void _linalg_check_errors(const at::Tensor & info, c10::string_view api_name, bool is_matrix); // {"schema": "aten::_linalg_check_errors(Tensor info, str api_name, *, bool is_matrix) -> ()", "dispatch": "True", "default": "True"}
at::Tensor & linalg_solve_triangular_out(const at::Tensor & self, const at::Tensor & B, bool upper, bool left, bool unitriangular, at::Tensor & out); // {"schema": "aten::linalg_solve_triangular.out(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor linalg_solve_triangular(const at::Tensor & self, const at::Tensor & B, bool upper, bool left, bool unitriangular); // {"schema": "aten::linalg_solve_triangular(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor linalg_vander(const at::Tensor & x, ::std::optional<c10::SymInt> N); // {"schema": "aten::linalg_vander(Tensor x, *, SymInt? N=None) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> svd_out(const at::Tensor & self, bool some, bool compute_uv, at::Tensor & U, at::Tensor & S, at::Tensor & V); // {"schema": "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)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> svd(const at::Tensor & self, bool some, bool compute_uv); // {"schema": "aten::svd(Tensor self, bool some=True, bool compute_uv=True) -> (Tensor U, Tensor S, Tensor V)", "dispatch": "False", "default": "True"}
at::Tensor swapaxes(const at::Tensor & self, int64_t axis0, int64_t axis1); // {"schema": "aten::swapaxes(Tensor(a) self, int axis0, int axis1) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor & swapaxes_(at::Tensor & self, int64_t axis0, int64_t axis1); // {"schema": "aten::swapaxes_(Tensor(a!) self, int axis0, int axis1) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor swapdims(const at::Tensor & self, int64_t dim0, int64_t dim1); // {"schema": "aten::swapdims(Tensor(a) self, int dim0, int dim1) -> Tensor(a)", "dispatch": "False", "default": "True"}
at::Tensor & swapdims_(at::Tensor & self, int64_t dim0, int64_t dim1); // {"schema": "aten::swapdims_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & cholesky_out(const at::Tensor & self, bool upper, at::Tensor & out); // {"schema": "aten::cholesky.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor cholesky(const at::Tensor & self, bool upper); // {"schema": "aten::cholesky(Tensor self, bool upper=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & cholesky_solve_out(const at::Tensor & self, const at::Tensor & input2, bool upper, at::Tensor & out); // {"schema": "aten::cholesky_solve.out(Tensor self, Tensor input2, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor cholesky_solve(const at::Tensor & self, const at::Tensor & input2, bool upper); // {"schema": "aten::cholesky_solve(Tensor self, Tensor input2, bool upper=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _cholesky_solve_helper(const at::Tensor & self, const at::Tensor & A, bool upper); // {"schema": "aten::_cholesky_solve_helper(Tensor self, Tensor A, bool upper) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor cholesky_inverse(const at::Tensor & self, bool upper); // {"schema": "aten::cholesky_inverse(Tensor self, bool upper=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & cholesky_inverse_out(const at::Tensor & self, bool upper, at::Tensor & out); // {"schema": "aten::cholesky_inverse.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor &,at::Tensor &> qr_out(const at::Tensor & self, bool some, at::Tensor & Q, at::Tensor & R); // {"schema": "aten::qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> qr(const at::Tensor & self, bool some); // {"schema": "aten::qr(Tensor self, bool some=True) -> (Tensor Q, Tensor R)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> geqrf_out(const at::Tensor & self, at::Tensor & a, at::Tensor & tau); // {"schema": "aten::geqrf.a(Tensor self, *, Tensor(a!) a, Tensor(b!) tau) -> (Tensor(a!) a, Tensor(b!) tau)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> geqrf(const at::Tensor & self); // {"schema": "aten::geqrf(Tensor self) -> (Tensor a, Tensor tau)", "dispatch": "True", "default": "False"}
at::Tensor orgqr(const at::Tensor & self, const at::Tensor & input2); // {"schema": "aten::orgqr(Tensor self, Tensor input2) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & orgqr_out(const at::Tensor & self, const at::Tensor & input2, at::Tensor & out); // {"schema": "aten::orgqr.out(Tensor self, Tensor input2, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & ormqr_out(const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose, at::Tensor & out); // {"schema": "aten::ormqr.out(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor ormqr(const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose); // {"schema": "aten::ormqr(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _lu_with_info(const at::Tensor & self, bool pivot, bool check_errors); // {"schema": "aten::_lu_with_info(Tensor self, bool pivot=True, bool check_errors=True) -> (Tensor LU, Tensor pivots, Tensor info)", "dispatch": "False", "default": "True"}
at::Tensor & lu_solve_out(const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots, at::Tensor & out); // {"schema": "aten::lu_solve.out(Tensor self, Tensor LU_data, Tensor LU_pivots, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor lu_solve(const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots); // {"schema": "aten::lu_solve(Tensor self, Tensor LU_data, Tensor LU_pivots) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> lu_unpack(const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data, bool unpack_pivots); // {"schema": "aten::lu_unpack(Tensor LU_data, Tensor LU_pivots, bool unpack_data=True, bool unpack_pivots=True) -> (Tensor P, Tensor L, Tensor U)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> lu_unpack_out(const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data, bool unpack_pivots, at::Tensor & P, at::Tensor & L, at::Tensor & U); // {"schema": "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)", "dispatch": "True", "default": "False"}
at::Tensor & multinomial_out(const at::Tensor & self, c10::SymInt num_samples, bool replacement, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::multinomial.out(Tensor self, SymInt num_samples, bool replacement=False, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor multinomial(const at::Tensor & self, c10::SymInt num_samples, bool replacement, ::std::optional<at::Generator> generator); // {"schema": "aten::multinomial(Tensor self, SymInt num_samples, bool replacement=False, *, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & lgamma_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::lgamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & lgamma_(at::Tensor & self); // {"schema": "aten::lgamma_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor lgamma(const at::Tensor & self); // {"schema": "aten::lgamma(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & digamma_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor digamma(const at::Tensor & self); // {"schema": "aten::digamma(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & polygamma_out(int64_t n, const at::Tensor & self, at::Tensor & out); // {"schema": "aten::polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor polygamma(int64_t n, const at::Tensor & self); // {"schema": "aten::polygamma(int n, Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & polygamma_(at::Tensor & self, int64_t n); // {"schema": "aten::polygamma_(Tensor(a!) self, int n) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor erfinv(const at::Tensor & self); // {"schema": "aten::erfinv(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & erfinv_(at::Tensor & self); // {"schema": "aten::erfinv_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & erfinv_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor i0(const at::Tensor & self); // {"schema": "aten::i0(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & i0_(at::Tensor & self); // {"schema": "aten::i0_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & i0_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor sign(const at::Tensor & self); // {"schema": "aten::sign(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & sign_(at::Tensor & self); // {"schema": "aten::sign_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & sign_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::sign.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor signbit(const at::Tensor & self); // {"schema": "aten::signbit(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & signbit_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::signbit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor dist(const at::Tensor & self, const at::Tensor & other, const at::Scalar & p); // {"schema": "aten::dist(Tensor self, Tensor other, Scalar p=2) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & atan2_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & atan2_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::atan2_(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor atan2(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::atan2(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor arctan2(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::arctan2(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & arctan2_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::arctan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & arctan2_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::arctan2_(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & lerp_out(const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight, at::Tensor & out); // {"schema": "aten::lerp.Scalar_out(Tensor self, Tensor end, Scalar weight, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & lerp_out(const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight, at::Tensor & out); // {"schema": "aten::lerp.Tensor_out(Tensor self, Tensor end, Tensor weight, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor lerp(const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight); // {"schema": "aten::lerp.Scalar(Tensor self, Tensor end, Scalar weight) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor lerp(const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight); // {"schema": "aten::lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & histc_out(const at::Tensor & self, int64_t bins, const at::Scalar & min, const at::Scalar & max, at::Tensor & out); // {"schema": "aten::histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor histc(const at::Tensor & self, int64_t bins, const at::Scalar & min, const at::Scalar & max); // {"schema": "aten::histc(Tensor self, int bins=100, Scalar min=0, Scalar max=0) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor &,at::Tensor &> histogram_out(const at::Tensor & self, const at::Tensor & bins, const ::std::optional<at::Tensor> & weight, bool density, at::Tensor & hist, at::Tensor & bin_edges); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> histogram(const at::Tensor & self, const at::Tensor & bins, const ::std::optional<at::Tensor> & weight, bool density); // {"schema": "aten::histogram.bins_tensor(Tensor self, Tensor bins, *, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor bin_edges)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor &,at::Tensor &> histogram_out(const at::Tensor & self, int64_t bins, ::std::optional<at::ArrayRef<double>> range, const ::std::optional<at::Tensor> & weight, bool density, at::Tensor & hist, at::Tensor & bin_edges); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> histogram(const at::Tensor & self, int64_t bins, ::std::optional<at::ArrayRef<double>> range, const ::std::optional<at::Tensor> & weight, bool density); // {"schema": "aten::histogram.bin_ct(Tensor self, int bins=100, *, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor bin_edges)", "dispatch": "True", "default": "False"}
::std::vector<at::Tensor> _histogramdd_bin_edges(const at::Tensor & self, at::IntArrayRef bins, ::std::optional<at::ArrayRef<double>> range, const ::std::optional<at::Tensor> & weight, bool density); // {"schema": "aten::_histogramdd_bin_edges(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False) -> Tensor[]", "dispatch": "True", "default": "False"}
at::Tensor _histogramdd_from_bin_cts(const at::Tensor & self, at::IntArrayRef bins, ::std::optional<at::ArrayRef<double>> range, const ::std::optional<at::Tensor> & weight, bool density); // {"schema": "aten::_histogramdd_from_bin_cts(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _histogramdd_from_bin_tensors(const at::Tensor & self, at::TensorList bins, const ::std::optional<at::Tensor> & weight, bool density); // {"schema": "aten::_histogramdd_from_bin_tensors(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,::std::vector<at::Tensor>> histogramdd(const at::Tensor & self, at::IntArrayRef bins, ::std::optional<at::ArrayRef<double>> range, const ::std::optional<at::Tensor> & weight, bool density); // {"schema": "aten::histogramdd(Tensor self, int[] bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,::std::vector<at::Tensor>> histogramdd(const at::Tensor & self, int64_t bins, ::std::optional<at::ArrayRef<double>> range, const ::std::optional<at::Tensor> & weight, bool density); // {"schema": "aten::histogramdd.int_bins(Tensor self, int bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,::std::vector<at::Tensor>> histogramdd(const at::Tensor & self, at::TensorList bins, ::std::optional<at::ArrayRef<double>> range, const ::std::optional<at::Tensor> & weight, bool density); // {"schema": "aten::histogramdd.TensorList_bins(Tensor self, Tensor[] bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges)", "dispatch": "False", "default": "True"}
at::Tensor & fmod_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::fmod.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor fmod(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::fmod.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & fmod_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::fmod_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & fmod_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::fmod.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor fmod(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::fmod.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & fmod_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::fmod_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & hypot_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::hypot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor hypot(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::hypot(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & hypot_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::hypot_(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & igamma_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::igamma.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor igamma(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::igamma(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & igamma_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::igamma_(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & igammac_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::igammac.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor igammac(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::igammac(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & igammac_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::igammac_(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & nextafter_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::nextafter.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor nextafter(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::nextafter(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & nextafter_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::nextafter_(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & remainder_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::remainder.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor remainder(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::remainder.Scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & remainder_(at::Tensor & self, const at::Scalar & other); // {"schema": "aten::remainder_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & remainder_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::remainder.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor remainder(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::remainder.Tensor(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & remainder_(at::Tensor & self, const at::Tensor & other); // {"schema": "aten::remainder_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor remainder(const at::Scalar & self, const at::Tensor & other); // {"schema": "aten::remainder.Scalar_Tensor(Scalar self, Tensor other) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor min(const at::Tensor & self); // {"schema": "aten::min(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & min_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::min.unary_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor fmin(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::fmin(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & fmin_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor max(const at::Tensor & self); // {"schema": "aten::max(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor fmax(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::fmax(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & fmax_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::fmax.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor maximum(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::maximum(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & maximum_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::maximum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor max(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::max.other(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & max_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::max.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & max_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::max.unary_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor minimum(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::minimum(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & minimum_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::minimum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & min_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::min.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor min(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::min.other(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor quantile(const at::Tensor & self, const at::Tensor & q, ::std::optional<int64_t> dim, bool keepdim, c10::string_view interpolation); // {"schema": "aten::quantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & quantile_out(const at::Tensor & self, const at::Tensor & q, ::std::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out); // {"schema": "aten::quantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor quantile(const at::Tensor & self, double q, ::std::optional<int64_t> dim, bool keepdim, c10::string_view interpolation); // {"schema": "aten::quantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & quantile_out(const at::Tensor & self, double q, ::std::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out); // {"schema": "aten::quantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor nanquantile(const at::Tensor & self, const at::Tensor & q, ::std::optional<int64_t> dim, bool keepdim, c10::string_view interpolation); // {"schema": "aten::nanquantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & nanquantile_out(const at::Tensor & self, const at::Tensor & q, ::std::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out); // {"schema": "aten::nanquantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor nanquantile(const at::Tensor & self, double q, ::std::optional<int64_t> dim, bool keepdim, c10::string_view interpolation); // {"schema": "aten::nanquantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & nanquantile_out(const at::Tensor & self, double q, ::std::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out); // {"schema": "aten::nanquantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> sort_out(const at::Tensor & self, int64_t dim, bool descending, at::Tensor & values, at::Tensor & indices); // {"schema": "aten::sort.values(Tensor self, int dim=-1, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> sort_out(const at::Tensor & self, ::std::optional<bool> stable, int64_t dim, bool descending, at::Tensor & values, at::Tensor & indices); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> sort(const at::Tensor & self, int64_t dim, bool descending); // {"schema": "aten::sort(Tensor self, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> sort(const at::Tensor & self, ::std::optional<bool> stable, int64_t dim, bool descending); // {"schema": "aten::sort.stable(Tensor self, *, bool? stable, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices)", "dispatch": "True", "default": "True"}
at::Tensor & msort_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::msort.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor msort(const at::Tensor & self); // {"schema": "aten::msort(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor argsort(const at::Tensor & self, int64_t dim, bool descending); // {"schema": "aten::argsort(Tensor self, int dim=-1, bool descending=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor argsort(const at::Tensor & self, bool stable, int64_t dim, bool descending); // {"schema": "aten::argsort.stable(Tensor self, *, bool stable, int dim=-1, bool descending=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & argsort_out(const at::Tensor & self, bool stable, int64_t dim, bool descending, at::Tensor & out); // {"schema": "aten::argsort.stable_out(Tensor self, *, bool stable, int dim=-1, bool descending=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> topk_out(const at::Tensor & self, c10::SymInt k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> topk(const at::Tensor & self, c10::SymInt k, int64_t dim, bool largest, bool sorted); // {"schema": "aten::topk(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)", "dispatch": "True", "default": "True"}
at::Tensor all(const at::Tensor & self); // {"schema": "aten::all(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & all_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::all.all_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor any(const at::Tensor & self); // {"schema": "aten::any(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & any_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::any.all_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & renorm_out(const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm, at::Tensor & out); // {"schema": "aten::renorm.out(Tensor self, Scalar p, int dim, Scalar maxnorm, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor renorm(const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm); // {"schema": "aten::renorm(Tensor self, Scalar p, int dim, Scalar maxnorm) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & renorm_(at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm); // {"schema": "aten::renorm_(Tensor(a!) self, Scalar p, int dim, Scalar maxnorm) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor unfold(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step); // {"schema": "aten::unfold(Tensor(a) self, int dimension, int size, int step) -> Tensor(a)", "dispatch": "True", "default": "False"}
at::Tensor unfold_backward(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step); // {"schema": "aten::unfold_backward(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step) -> Tensor", "dispatch": "True", "default": "False"}
bool equal(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::equal(Tensor self, Tensor other) -> bool", "dispatch": "True", "default": "False"}
at::Tensor & pow_out(const at::Tensor & self, const at::Tensor & exponent, at::Tensor & out); // {"schema": "aten::pow.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor pow(const at::Tensor & self, const at::Tensor & exponent); // {"schema": "aten::pow.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & pow_out(const at::Scalar & self, const at::Tensor & exponent, at::Tensor & out); // {"schema": "aten::pow.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor pow(const at::Scalar & self, const at::Tensor & exponent); // {"schema": "aten::pow.Scalar(Scalar self, Tensor exponent) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & pow_out(const at::Tensor & self, const at::Scalar & exponent, at::Tensor & out); // {"schema": "aten::pow.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor pow(const at::Tensor & self, const at::Scalar & exponent); // {"schema": "aten::pow.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & pow_(at::Tensor & self, const at::Scalar & exponent); // {"schema": "aten::pow_.Scalar(Tensor(a!) self, Scalar exponent) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & pow_(at::Tensor & self, const at::Tensor & exponent); // {"schema": "aten::pow_.Tensor(Tensor(a!) self, Tensor exponent) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & float_power_out(const at::Tensor & self, const at::Tensor & exponent, at::Tensor & out); // {"schema": "aten::float_power.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor float_power(const at::Tensor & self, const at::Tensor & exponent); // {"schema": "aten::float_power.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & float_power_out(const at::Scalar & self, const at::Tensor & exponent, at::Tensor & out); // {"schema": "aten::float_power.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor float_power(const at::Scalar & self, const at::Tensor & exponent); // {"schema": "aten::float_power.Scalar(Scalar self, Tensor exponent) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & float_power_out(const at::Tensor & self, const at::Scalar & exponent, at::Tensor & out); // {"schema": "aten::float_power.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor float_power(const at::Tensor & self, const at::Scalar & exponent); // {"schema": "aten::float_power.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & float_power_(at::Tensor & self, const at::Scalar & exponent); // {"schema": "aten::float_power_.Scalar(Tensor(a!) self, Scalar exponent) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & float_power_(at::Tensor & self, const at::Tensor & exponent); // {"schema": "aten::float_power_.Tensor(Tensor(a!) self, Tensor exponent) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & normal_(at::Tensor & self, double mean, double std, ::std::optional<at::Generator> generator); // {"schema": "aten::normal_(Tensor(a!) self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor normal_functional(const at::Tensor & self, double mean, double std, ::std::optional<at::Generator> generator); // {"schema": "aten::normal_functional(Tensor self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & normal_out(const at::Tensor & mean, double std, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::normal.Tensor_float_out(Tensor mean, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor normal(const at::Tensor & mean, double std, ::std::optional<at::Generator> generator); // {"schema": "aten::normal.Tensor_float(Tensor mean, float std=1, *, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & normal_out(double mean, const at::Tensor & std, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::normal.float_Tensor_out(float mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor normal(double mean, const at::Tensor & std, ::std::optional<at::Generator> generator); // {"schema": "aten::normal.float_Tensor(float mean, Tensor std, *, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & normal_out(const at::Tensor & mean, const at::Tensor & std, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::normal.Tensor_Tensor_out(Tensor mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor normal(const at::Tensor & mean, const at::Tensor & std, ::std::optional<at::Generator> generator); // {"schema": "aten::normal.Tensor_Tensor(Tensor mean, Tensor std, *, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor normal(double mean, double std, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "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", "dispatch": "True", "default": "True"}
at::Tensor & normal_out(double mean, double std, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor alias(const at::Tensor & self); // {"schema": "aten::alias(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "True"}
void _amp_foreach_non_finite_check_and_unscale_(at::TensorList self, at::Tensor & found_inf, const at::Tensor & inv_scale); // {"schema": "aten::_amp_foreach_non_finite_check_and_unscale_(Tensor(a!)[] self, Tensor(b!) found_inf, Tensor inv_scale) -> ()", "dispatch": "True", "default": "False"}
at::Tensor & _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); // {"schema": "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!)", "dispatch": "True", "default": "False"}
::std::vector<at::Tensor> _foreach_add(at::TensorList self, const at::Scalar & scalar); // {"schema": "aten::_foreach_add.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_add_(at::TensorList self, const at::Scalar & scalar); // {"schema": "aten::_foreach_add_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_add(at::TensorList self, at::TensorList other, const at::Scalar & alpha); // {"schema": "aten::_foreach_add.List(Tensor[] self, Tensor[] other, *, Scalar alpha=1) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_add_(at::TensorList self, at::TensorList other, const at::Scalar & alpha); // {"schema": "aten::_foreach_add_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_add(at::TensorList self, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_add.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_add_(at::TensorList self, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_add_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_add(at::TensorList self, const at::Tensor & other, const at::Scalar & alpha); // {"schema": "aten::_foreach_add.Tensor(Tensor[] self, Tensor other, *, Scalar alpha=1) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_add_(at::TensorList self, const at::Tensor & other, const at::Scalar & alpha); // {"schema": "aten::_foreach_add_.Tensor(Tensor(a!)[] self, Tensor other, *, Scalar alpha=1) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_sub(at::TensorList self, const at::Scalar & scalar); // {"schema": "aten::_foreach_sub.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_sub_(at::TensorList self, const at::Scalar & scalar); // {"schema": "aten::_foreach_sub_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_sub(at::TensorList self, at::TensorList other, const at::Scalar & alpha); // {"schema": "aten::_foreach_sub.List(Tensor[] self, Tensor[] other, *, Scalar alpha=1) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_sub_(at::TensorList self, at::TensorList other, const at::Scalar & alpha); // {"schema": "aten::_foreach_sub_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_sub(at::TensorList self, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_sub.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_sub_(at::TensorList self, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_sub_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_mul(at::TensorList self, const at::Scalar & scalar); // {"schema": "aten::_foreach_mul.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_mul_(at::TensorList self, const at::Scalar & scalar); // {"schema": "aten::_foreach_mul_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_mul(at::TensorList self, at::TensorList other); // {"schema": "aten::_foreach_mul.List(Tensor[] self, Tensor[] other) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_mul_(at::TensorList self, at::TensorList other); // {"schema": "aten::_foreach_mul_.List(Tensor(a!)[] self, Tensor[] other) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_mul(at::TensorList self, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_mul.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_mul_(at::TensorList self, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_mul_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_mul(at::TensorList self, const at::Tensor & other); // {"schema": "aten::_foreach_mul.Tensor(Tensor[] self, Tensor other) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_mul_(at::TensorList self, const at::Tensor & other); // {"schema": "aten::_foreach_mul_.Tensor(Tensor(a!)[] self, Tensor other) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_div(at::TensorList self, const at::Scalar & scalar); // {"schema": "aten::_foreach_div.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_div_(at::TensorList self, const at::Scalar & scalar); // {"schema": "aten::_foreach_div_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_div(at::TensorList self, at::TensorList other); // {"schema": "aten::_foreach_div.List(Tensor[] self, Tensor[] other) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_div_(at::TensorList self, at::TensorList other); // {"schema": "aten::_foreach_div_.List(Tensor(a!)[] self, Tensor[] other) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_div(at::TensorList self, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_div.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_div_(at::TensorList self, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_div_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_div(at::TensorList self, const at::Tensor & other); // {"schema": "aten::_foreach_div.Tensor(Tensor[] self, Tensor other) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_div_(at::TensorList self, const at::Tensor & other); // {"schema": "aten::_foreach_div_.Tensor(Tensor(a!)[] self, Tensor other) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_clamp_max(at::TensorList self, const at::Scalar & scalar); // {"schema": "aten::_foreach_clamp_max.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_clamp_max_(at::TensorList self, const at::Scalar & scalar); // {"schema": "aten::_foreach_clamp_max_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_clamp_max(at::TensorList self, at::TensorList other); // {"schema": "aten::_foreach_clamp_max.List(Tensor[] self, Tensor[] other) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_clamp_max_(at::TensorList self, at::TensorList other); // {"schema": "aten::_foreach_clamp_max_.List(Tensor(a!)[] self, Tensor[] other) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_clamp_max(at::TensorList self, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_clamp_max.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_clamp_max_(at::TensorList self, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_clamp_max_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_clamp_min(at::TensorList self, const at::Scalar & scalar); // {"schema": "aten::_foreach_clamp_min.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_clamp_min_(at::TensorList self, const at::Scalar & scalar); // {"schema": "aten::_foreach_clamp_min_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_clamp_min(at::TensorList self, at::TensorList other); // {"schema": "aten::_foreach_clamp_min.List(Tensor[] self, Tensor[] other) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_clamp_min_(at::TensorList self, at::TensorList other); // {"schema": "aten::_foreach_clamp_min_.List(Tensor(a!)[] self, Tensor[] other) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_clamp_min(at::TensorList self, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_clamp_min.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_clamp_min_(at::TensorList self, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_clamp_min_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_maximum(at::TensorList self, const at::Scalar & scalar); // {"schema": "aten::_foreach_maximum.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_maximum_(at::TensorList self, const at::Scalar & scalar); // {"schema": "aten::_foreach_maximum_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_maximum(at::TensorList self, at::TensorList other); // {"schema": "aten::_foreach_maximum.List(Tensor[] self, Tensor[] other) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_maximum_(at::TensorList self, at::TensorList other); // {"schema": "aten::_foreach_maximum_.List(Tensor(a!)[] self, Tensor[] other) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_maximum(at::TensorList self, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_maximum.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_maximum_(at::TensorList self, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_maximum_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_minimum(at::TensorList self, const at::Scalar & scalar); // {"schema": "aten::_foreach_minimum.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_minimum_(at::TensorList self, const at::Scalar & scalar); // {"schema": "aten::_foreach_minimum_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_minimum(at::TensorList self, at::TensorList other); // {"schema": "aten::_foreach_minimum.List(Tensor[] self, Tensor[] other) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_minimum_(at::TensorList self, at::TensorList other); // {"schema": "aten::_foreach_minimum_.List(Tensor(a!)[] self, Tensor[] other) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_minimum(at::TensorList self, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_minimum.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_minimum_(at::TensorList self, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_minimum_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_addcdiv(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value); // {"schema": "aten::_foreach_addcdiv.Scalar(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[]", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_addcdiv(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_addcdiv.ScalarList(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[]", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_addcdiv(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars); // {"schema": "aten::_foreach_addcdiv.Tensor(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_addcdiv_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value); // {"schema": "aten::_foreach_addcdiv_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> ()", "dispatch": "True", "default": "True"}
void _foreach_addcdiv_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_addcdiv_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> ()", "dispatch": "True", "default": "True"}
void _foreach_addcdiv_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars); // {"schema": "aten::_foreach_addcdiv_.Tensor(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_addcmul(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value); // {"schema": "aten::_foreach_addcmul.Scalar(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[]", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_addcmul(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_addcmul.ScalarList(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[]", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_addcmul(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars); // {"schema": "aten::_foreach_addcmul.Tensor(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_addcmul_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value); // {"schema": "aten::_foreach_addcmul_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> ()", "dispatch": "True", "default": "True"}
void _foreach_addcmul_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars); // {"schema": "aten::_foreach_addcmul_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> ()", "dispatch": "True", "default": "True"}
void _foreach_addcmul_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars); // {"schema": "aten::_foreach_addcmul_.Tensor(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_abs(at::TensorList self); // {"schema": "aten::_foreach_abs(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_abs_(at::TensorList self); // {"schema": "aten::_foreach_abs_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_acos(at::TensorList self); // {"schema": "aten::_foreach_acos(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_acos_(at::TensorList self); // {"schema": "aten::_foreach_acos_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_asin(at::TensorList self); // {"schema": "aten::_foreach_asin(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_asin_(at::TensorList self); // {"schema": "aten::_foreach_asin_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_atan(at::TensorList self); // {"schema": "aten::_foreach_atan(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_atan_(at::TensorList self); // {"schema": "aten::_foreach_atan_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_ceil(at::TensorList self); // {"schema": "aten::_foreach_ceil(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_ceil_(at::TensorList self); // {"schema": "aten::_foreach_ceil_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_cos(at::TensorList self); // {"schema": "aten::_foreach_cos(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_cos_(at::TensorList self); // {"schema": "aten::_foreach_cos_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_cosh(at::TensorList self); // {"schema": "aten::_foreach_cosh(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_cosh_(at::TensorList self); // {"schema": "aten::_foreach_cosh_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_erf(at::TensorList self); // {"schema": "aten::_foreach_erf(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_erf_(at::TensorList self); // {"schema": "aten::_foreach_erf_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_erfc(at::TensorList self); // {"schema": "aten::_foreach_erfc(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_erfc_(at::TensorList self); // {"schema": "aten::_foreach_erfc_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_exp(at::TensorList self); // {"schema": "aten::_foreach_exp(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_exp_(at::TensorList self); // {"schema": "aten::_foreach_exp_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_expm1(at::TensorList self); // {"schema": "aten::_foreach_expm1(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_expm1_(at::TensorList self); // {"schema": "aten::_foreach_expm1_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_floor(at::TensorList self); // {"schema": "aten::_foreach_floor(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_floor_(at::TensorList self); // {"schema": "aten::_foreach_floor_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_frac(at::TensorList self); // {"schema": "aten::_foreach_frac(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_frac_(at::TensorList self); // {"schema": "aten::_foreach_frac_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_lerp(at::TensorList self, at::TensorList tensors1, at::TensorList weights); // {"schema": "aten::_foreach_lerp.List(Tensor[] self, Tensor[] tensors1, Tensor[] weights) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_lerp_(at::TensorList self, at::TensorList tensors1, at::TensorList weights); // {"schema": "aten::_foreach_lerp_.List(Tensor(a!)[] self, Tensor[] tensors1, Tensor[] weights) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_lerp(at::TensorList self, at::TensorList tensors1, const at::Scalar & weight); // {"schema": "aten::_foreach_lerp.Scalar(Tensor[] self, Tensor[] tensors1, Scalar weight) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_lerp_(at::TensorList self, at::TensorList tensors1, const at::Scalar & weight); // {"schema": "aten::_foreach_lerp_.Scalar(Tensor(a!)[] self, Tensor[] tensors1, Scalar weight) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_lerp(at::TensorList self, at::TensorList tensors1, at::ArrayRef<at::Scalar> weight); // {"schema": "aten::_foreach_lerp.ScalarList(Tensor[] self, Tensor[] tensors1, Scalar[] weight) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_lerp_(at::TensorList self, at::TensorList tensors1, at::ArrayRef<at::Scalar> weight); // {"schema": "aten::_foreach_lerp_.ScalarList(Tensor(a!)[] self, Tensor[] tensors1, Scalar[] weight) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_lgamma(at::TensorList self); // {"schema": "aten::_foreach_lgamma(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_lgamma_(at::TensorList self); // {"schema": "aten::_foreach_lgamma_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_log(at::TensorList self); // {"schema": "aten::_foreach_log(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_log_(at::TensorList self); // {"schema": "aten::_foreach_log_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_log10(at::TensorList self); // {"schema": "aten::_foreach_log10(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_log10_(at::TensorList self); // {"schema": "aten::_foreach_log10_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_log1p(at::TensorList self); // {"schema": "aten::_foreach_log1p(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_log1p_(at::TensorList self); // {"schema": "aten::_foreach_log1p_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_log2(at::TensorList self); // {"schema": "aten::_foreach_log2(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_log2_(at::TensorList self); // {"schema": "aten::_foreach_log2_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_max(at::TensorList self); // {"schema": "aten::_foreach_max(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_neg(at::TensorList self); // {"schema": "aten::_foreach_neg(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_neg_(at::TensorList self); // {"schema": "aten::_foreach_neg_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_norm(at::TensorList self, const at::Scalar & ord, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::_foreach_norm.Scalar(Tensor[] self, Scalar ord=2, ScalarType? dtype=None) -> Tensor[]", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_powsum(at::TensorList self, const at::Scalar & ord, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::_foreach_powsum.Scalar(Tensor[] self, Scalar ord=2, ScalarType? dtype=None) -> Tensor[]", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_pow(at::TensorList self, at::TensorList exponent); // {"schema": "aten::_foreach_pow.List(Tensor[] self, Tensor[] exponent) -> Tensor[]", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_pow(at::TensorList self, const at::Scalar & exponent); // {"schema": "aten::_foreach_pow.Scalar(Tensor[] self, Scalar exponent) -> Tensor[]", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_pow(at::TensorList self, at::ArrayRef<at::Scalar> exponent); // {"schema": "aten::_foreach_pow.ScalarList(Tensor[] self, Scalar[] exponent) -> Tensor[]", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_pow(const at::Scalar & self, at::TensorList exponent); // {"schema": "aten::_foreach_pow.ScalarAndTensor(Scalar self, Tensor[] exponent) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_pow_(at::TensorList self, at::TensorList exponent); // {"schema": "aten::_foreach_pow_.List(Tensor(a!)[] self, Tensor[] exponent) -> ()", "dispatch": "True", "default": "True"}
void _foreach_pow_(at::TensorList self, const at::Scalar & exponent); // {"schema": "aten::_foreach_pow_.Scalar(Tensor(a!)[] self, Scalar exponent) -> ()", "dispatch": "True", "default": "True"}
void _foreach_pow_(at::TensorList self, at::ArrayRef<at::Scalar> exponent); // {"schema": "aten::_foreach_pow_.ScalarList(Tensor(a!)[] self, Scalar[] exponent) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_reciprocal(at::TensorList self); // {"schema": "aten::_foreach_reciprocal(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_reciprocal_(at::TensorList self); // {"schema": "aten::_foreach_reciprocal_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_round(at::TensorList self); // {"schema": "aten::_foreach_round(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_round_(at::TensorList self); // {"schema": "aten::_foreach_round_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_rsqrt(at::TensorList self); // {"schema": "aten::_foreach_rsqrt(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_rsqrt_(at::TensorList self); // {"schema": "aten::_foreach_rsqrt_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_sigmoid(at::TensorList self); // {"schema": "aten::_foreach_sigmoid(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_sigmoid_(at::TensorList self); // {"schema": "aten::_foreach_sigmoid_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_sign(at::TensorList self); // {"schema": "aten::_foreach_sign(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_sign_(at::TensorList self); // {"schema": "aten::_foreach_sign_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_sin(at::TensorList self); // {"schema": "aten::_foreach_sin(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_sin_(at::TensorList self); // {"schema": "aten::_foreach_sin_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_sinh(at::TensorList self); // {"schema": "aten::_foreach_sinh(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_sinh_(at::TensorList self); // {"schema": "aten::_foreach_sinh_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_sqrt(at::TensorList self); // {"schema": "aten::_foreach_sqrt(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_sqrt_(at::TensorList self); // {"schema": "aten::_foreach_sqrt_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_tan(at::TensorList self); // {"schema": "aten::_foreach_tan(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_tan_(at::TensorList self); // {"schema": "aten::_foreach_tan_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_tanh(at::TensorList self); // {"schema": "aten::_foreach_tanh(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_tanh_(at::TensorList self); // {"schema": "aten::_foreach_tanh_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_trunc(at::TensorList self); // {"schema": "aten::_foreach_trunc(Tensor[] self) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_trunc_(at::TensorList self); // {"schema": "aten::_foreach_trunc_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
void _foreach_zero_(at::TensorList self); // {"schema": "aten::_foreach_zero_(Tensor(a!)[] self) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_clone(at::TensorList self, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::_foreach_clone(Tensor[] self, *, MemoryFormat? memory_format=None) -> Tensor[]", "dispatch": "True", "default": "True"}
void _foreach_copy_(at::TensorList self, at::TensorList src, bool non_blocking); // {"schema": "aten::_foreach_copy_(Tensor(a!)[] self, Tensor[] src, bool non_blocking=False) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_copy(at::TensorList self, at::TensorList src, bool non_blocking); // {"schema": "aten::_foreach_copy(Tensor[] self, Tensor[] src, bool non_blocking=False) -> Tensor[] self_out", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_mm(at::TensorList self, at::TensorList mat2); // {"schema": "aten::_foreach_mm(Tensor[] self, Tensor[] mat2) -> Tensor[]", "dispatch": "True", "default": "True"}
at::Tensor bucketize(const at::Tensor & self, const at::Tensor & boundaries, bool out_int32, bool right); // {"schema": "aten::bucketize.Tensor(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & bucketize_out(const at::Tensor & self, const at::Tensor & boundaries, bool out_int32, bool right, at::Tensor & out); // {"schema": "aten::bucketize.Tensor_out(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor bucketize(const at::Scalar & self, const at::Tensor & boundaries, bool out_int32, bool right); // {"schema": "aten::bucketize.Scalar(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor searchsorted(const at::Tensor & sorted_sequence, const at::Tensor & self, bool out_int32, bool right, ::std::optional<c10::string_view> side, const ::std::optional<at::Tensor> & sorter); // {"schema": "aten::searchsorted.Tensor(Tensor sorted_sequence, Tensor self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & searchsorted_out(const at::Tensor & sorted_sequence, const at::Tensor & self, bool out_int32, bool right, ::std::optional<c10::string_view> side, const ::std::optional<at::Tensor> & sorter, at::Tensor & out); // {"schema": "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!)", "dispatch": "True", "default": "False"}
at::Tensor searchsorted(const at::Tensor & sorted_sequence, const at::Scalar & self, bool out_int32, bool right, ::std::optional<c10::string_view> side, const ::std::optional<at::Tensor> & sorter); // {"schema": "aten::searchsorted.Scalar(Tensor sorted_sequence, Scalar self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & searchsorted_out(const at::Tensor & sorted_sequence, const at::Scalar & self, bool out_int32, bool right, ::std::optional<c10::string_view> side, const ::std::optional<at::Tensor> & sorter, at::Tensor & out); // {"schema": "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!)", "dispatch": "True", "default": "False"}
at::Tensor _convert_indices_from_coo_to_csr(const at::Tensor & self, int64_t size, bool out_int32); // {"schema": "aten::_convert_indices_from_coo_to_csr(Tensor self, int size, *, bool out_int32=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _convert_indices_from_coo_to_csr_out(const at::Tensor & self, int64_t size, bool out_int32, at::Tensor & out); // {"schema": "aten::_convert_indices_from_coo_to_csr.out(Tensor self, int size, *, bool out_int32=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _convert_indices_from_csr_to_coo(const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose); // {"schema": "aten::_convert_indices_from_csr_to_coo(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _convert_indices_from_csr_to_coo_out(const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose, at::Tensor & out); // {"schema": "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!)", "dispatch": "True", "default": "False"}
at::Tensor & mse_loss_out(const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & out); // {"schema": "aten::mse_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor mse_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction); // {"schema": "aten::mse_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & mse_loss_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & grad_input); // {"schema": "aten::mse_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor mse_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction); // {"schema": "aten::mse_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor l1_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction); // {"schema": "aten::l1_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & multi_margin_loss_out(const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const ::std::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & out); // {"schema": "aten::multi_margin_loss.out(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor multi_margin_loss(const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const ::std::optional<at::Tensor> & weight, int64_t reduction); // {"schema": "aten::multi_margin_loss(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & multi_margin_loss_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const ::std::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & grad_input); // {"schema": "aten::multi_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Scalar p, Scalar margin, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor multi_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const ::std::optional<at::Tensor> & weight, int64_t reduction); // {"schema": "aten::multi_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, Scalar p, Scalar margin, Tensor? weight=None, int reduction=Mean) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & multilabel_margin_loss_out(const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & out); // {"schema": "aten::multilabel_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor multilabel_margin_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction); // {"schema": "aten::multilabel_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> multilabel_margin_loss_forward_out(const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & output, at::Tensor & is_target); // {"schema": "aten::multilabel_margin_loss_forward.output(Tensor self, Tensor target, int reduction, *, Tensor(a!) output, Tensor(b!) is_target) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> multilabel_margin_loss_forward(const at::Tensor & self, const at::Tensor & target, int64_t reduction); // {"schema": "aten::multilabel_margin_loss_forward(Tensor self, Tensor target, int reduction) -> (Tensor output, Tensor is_target)", "dispatch": "True", "default": "False"}
at::Tensor & multilabel_margin_loss_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target, at::Tensor & grad_input); // {"schema": "aten::multilabel_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor multilabel_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target); // {"schema": "aten::multilabel_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & nll_loss_out(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & out); // {"schema": "aten::nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor nll_loss_nd(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index); // {"schema": "aten::nll_loss_nd(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor nll_loss(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index); // {"schema": "aten::nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> nll_loss_forward_out(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & output, at::Tensor & total_weight); // {"schema": "aten::nll_loss_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> nll_loss_forward(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index); // {"schema": "aten::nll_loss_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight)", "dispatch": "True", "default": "True"}
at::Tensor & nll_loss_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input); // {"schema": "aten::nll_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor nll_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight); // {"schema": "aten::nll_loss_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & nll_loss2d_out(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & out); // {"schema": "aten::nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor nll_loss2d(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index); // {"schema": "aten::nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> nll_loss2d_forward_out(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & output, at::Tensor & total_weight); // {"schema": "aten::nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> nll_loss2d_forward(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index); // {"schema": "aten::nll_loss2d_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight)", "dispatch": "True", "default": "False"}
at::Tensor & nll_loss2d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input); // {"schema": "aten::nll_loss2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor nll_loss2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight); // {"schema": "aten::nll_loss2d_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & smooth_l1_loss_out(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta, at::Tensor & out); // {"schema": "aten::smooth_l1_loss.out(Tensor self, Tensor target, int reduction=Mean, float beta=1.0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor smooth_l1_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta); // {"schema": "aten::smooth_l1_loss(Tensor self, Tensor target, int reduction=Mean, float beta=1.0) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & smooth_l1_loss_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta, at::Tensor & grad_input); // {"schema": "aten::smooth_l1_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor smooth_l1_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta); // {"schema": "aten::smooth_l1_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & huber_loss_out(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta, at::Tensor & out); // {"schema": "aten::huber_loss.out(Tensor self, Tensor target, int reduction=Mean, float delta=1.0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor huber_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta); // {"schema": "aten::huber_loss(Tensor self, Tensor target, int reduction=Mean, float delta=1.0) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & huber_loss_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta, at::Tensor & grad_input); // {"schema": "aten::huber_loss_backward.out(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor huber_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta); // {"schema": "aten::huber_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & soft_margin_loss_out(const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & out); // {"schema": "aten::soft_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor soft_margin_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction); // {"schema": "aten::soft_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & soft_margin_loss_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & grad_input); // {"schema": "aten::soft_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor soft_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction); // {"schema": "aten::soft_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & elu_out(const at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, at::Tensor & out); // {"schema": "aten::elu.out(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor elu(const at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale); // {"schema": "aten::elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & elu_backward_out(const at::Tensor & grad_output, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, bool is_result, const at::Tensor & self_or_result, at::Tensor & grad_input); // {"schema": "aten::elu_backward.grad_input(Tensor grad_output, Scalar alpha, Scalar scale, Scalar input_scale, bool is_result, Tensor self_or_result, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor elu_backward(const at::Tensor & grad_output, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, bool is_result, const at::Tensor & self_or_result); // {"schema": "aten::elu_backward(Tensor grad_output, Scalar alpha, Scalar scale, Scalar input_scale, bool is_result, Tensor self_or_result) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & elu_(at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale); // {"schema": "aten::elu_(Tensor(a!) self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & glu_out(const at::Tensor & self, int64_t dim, at::Tensor & out); // {"schema": "aten::glu.out(Tensor self, int dim=-1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor glu(const at::Tensor & self, int64_t dim); // {"schema": "aten::glu(Tensor self, int dim=-1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & glu_backward_out(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim, at::Tensor & grad_input); // {"schema": "aten::glu_backward.grad_input(Tensor grad_output, Tensor self, int dim, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor glu_backward(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim); // {"schema": "aten::glu_backward(Tensor grad_output, Tensor self, int dim) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor glu_jvp(const at::Tensor & glu, const at::Tensor & x, const at::Tensor & dx, int64_t dim); // {"schema": "aten::glu_jvp(Tensor glu, Tensor x, Tensor dx, int dim) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor glu_backward_jvp(const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim); // {"schema": "aten::glu_backward_jvp(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & hardsigmoid_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::hardsigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor hardsigmoid(const at::Tensor & self); // {"schema": "aten::hardsigmoid(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & hardsigmoid_(at::Tensor & self); // {"schema": "aten::hardsigmoid_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & hardsigmoid_backward_out(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input); // {"schema": "aten::hardsigmoid_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor hardsigmoid_backward(const at::Tensor & grad_output, const at::Tensor & self); // {"schema": "aten::hardsigmoid_backward(Tensor grad_output, Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & hardtanh_out(const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val, at::Tensor & out); // {"schema": "aten::hardtanh.out(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor hardtanh(const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val); // {"schema": "aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & hardtanh_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val, at::Tensor & grad_input); // {"schema": "aten::hardtanh_backward.grad_input(Tensor grad_output, Tensor self, Scalar min_val, Scalar max_val, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor hardtanh_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val); // {"schema": "aten::hardtanh_backward(Tensor grad_output, Tensor self, Scalar min_val, Scalar max_val) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & hardtanh_(at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val); // {"schema": "aten::hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & hardswish_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::hardswish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor hardswish(const at::Tensor & self); // {"schema": "aten::hardswish(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & hardswish_(at::Tensor & self); // {"schema": "aten::hardswish_(Tensor(a!) self) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor hardswish_backward(const at::Tensor & grad_output, const at::Tensor & self); // {"schema": "aten::hardswish_backward(Tensor grad_output, Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & leaky_relu_out(const at::Tensor & self, const at::Scalar & negative_slope, at::Tensor & out); // {"schema": "aten::leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor leaky_relu(const at::Tensor & self, const at::Scalar & negative_slope); // {"schema": "aten::leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & leaky_relu_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & negative_slope, bool self_is_result, at::Tensor & grad_input); // {"schema": "aten::leaky_relu_backward.grad_input(Tensor grad_output, Tensor self, Scalar negative_slope, bool self_is_result, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor leaky_relu_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & negative_slope, bool self_is_result); // {"schema": "aten::leaky_relu_backward(Tensor grad_output, Tensor self, Scalar negative_slope, bool self_is_result) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & leaky_relu_(at::Tensor & self, const at::Scalar & negative_slope); // {"schema": "aten::leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & log_sigmoid_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::log_sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor log_sigmoid(const at::Tensor & self); // {"schema": "aten::log_sigmoid(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> log_sigmoid_forward_out(const at::Tensor & self, at::Tensor & output, at::Tensor & buffer); // {"schema": "aten::log_sigmoid_forward.output(Tensor self, *, Tensor(a!) output, Tensor(b!) buffer) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> log_sigmoid_forward(const at::Tensor & self); // {"schema": "aten::log_sigmoid_forward(Tensor self) -> (Tensor output, Tensor buffer)", "dispatch": "True", "default": "False"}
at::Tensor & log_sigmoid_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer, at::Tensor & grad_input); // {"schema": "aten::log_sigmoid_backward.grad_input(Tensor grad_output, Tensor self, Tensor buffer, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor log_sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer); // {"schema": "aten::log_sigmoid_backward(Tensor grad_output, Tensor self, Tensor buffer) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & rrelu_with_noise_out(const at::Tensor & self, at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::rrelu_with_noise.out(Tensor self, Tensor(b!) noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor rrelu_with_noise(const at::Tensor & self, at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, ::std::optional<at::Generator> generator); // {"schema": "aten::rrelu_with_noise(Tensor self, Tensor(b!) noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor rrelu_with_noise_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, bool self_is_result); // {"schema": "aten::rrelu_with_noise_backward(Tensor grad_output, Tensor self, Tensor noise, Scalar lower, Scalar upper, bool training, bool self_is_result) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & rrelu_with_noise_(at::Tensor & self, at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, ::std::optional<at::Generator> generator); // {"schema": "aten::rrelu_with_noise_(Tensor(a!) self, Tensor(b!) noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & softplus_out(const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold, at::Tensor & out); // {"schema": "aten::softplus.out(Tensor self, Scalar beta=1, Scalar threshold=20, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor softplus(const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold); // {"schema": "aten::softplus(Tensor self, Scalar beta=1, Scalar threshold=20) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & softplus_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold, at::Tensor & grad_input); // {"schema": "aten::softplus_backward.grad_input(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor softplus_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold); // {"schema": "aten::softplus_backward(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & softshrink_out(const at::Tensor & self, const at::Scalar & lambd, at::Tensor & out); // {"schema": "aten::softshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor softshrink(const at::Tensor & self, const at::Scalar & lambd); // {"schema": "aten::softshrink(Tensor self, Scalar lambd=0.5) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & softshrink_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & grad_input); // {"schema": "aten::softshrink_backward.grad_input(Tensor grad_output, Tensor self, Scalar lambd, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor softshrink_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & lambd); // {"schema": "aten::softshrink_backward(Tensor grad_output, Tensor self, Scalar lambd) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & adaptive_avg_pool2d_out(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out); // {"schema": "aten::adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor adaptive_avg_pool2d(const at::Tensor & self, c10::SymIntArrayRef output_size); // {"schema": "aten::adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor mkldnn_adaptive_avg_pool2d(const at::Tensor & self, at::IntArrayRef output_size); // {"schema": "aten::mkldnn_adaptive_avg_pool2d(Tensor self, int[2] output_size) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & mkldnn_adaptive_avg_pool2d_out(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out); // {"schema": "aten::mkldnn_adaptive_avg_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor mkldnn_adaptive_avg_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self); // {"schema": "aten::mkldnn_adaptive_avg_pool2d_backward(Tensor grad_output, Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _adaptive_avg_pool2d(const at::Tensor & self, c10::SymIntArrayRef output_size); // {"schema": "aten::_adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _adaptive_avg_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self); // {"schema": "aten::_adaptive_avg_pool2d_backward(Tensor grad_output, Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & adaptive_avg_pool3d_out(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out); // {"schema": "aten::adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor adaptive_avg_pool3d(const at::Tensor & self, c10::SymIntArrayRef output_size); // {"schema": "aten::adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _adaptive_avg_pool3d(const at::Tensor & self, c10::SymIntArrayRef output_size); // {"schema": "aten::_adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & adaptive_avg_pool3d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input); // {"schema": "aten::adaptive_avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _adaptive_avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self); // {"schema": "aten::_adaptive_avg_pool3d_backward(Tensor grad_output, Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool2d_out(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out, at::Tensor & indices); // {"schema": "aten::adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> adaptive_max_pool2d(const at::Tensor & self, at::IntArrayRef output_size); // {"schema": "aten::adaptive_max_pool2d(Tensor self, int[2] output_size) -> (Tensor, Tensor)", "dispatch": "True", "default": "True"}
at::Tensor & adaptive_max_pool2d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::Tensor & grad_input); // {"schema": "aten::adaptive_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor adaptive_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices); // {"schema": "aten::adaptive_max_pool2d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool3d_out(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out, at::Tensor & indices); // {"schema": "aten::adaptive_max_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> adaptive_max_pool3d(const at::Tensor & self, at::IntArrayRef output_size); // {"schema": "aten::adaptive_max_pool3d(Tensor self, int[3] output_size) -> (Tensor, Tensor)", "dispatch": "True", "default": "True"}
at::Tensor & adaptive_max_pool3d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::Tensor & grad_input); // {"schema": "aten::adaptive_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor adaptive_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices); // {"schema": "aten::adaptive_max_pool3d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & avg_pool2d_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, ::std::optional<int64_t> divisor_override, at::Tensor & out); // {"schema": "aten::avg_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor avg_pool2d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, ::std::optional<int64_t> divisor_override); // {"schema": "aten::avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & avg_pool2d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, ::std::optional<int64_t> divisor_override, at::Tensor & grad_input); // {"schema": "aten::avg_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor avg_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, ::std::optional<int64_t> divisor_override); // {"schema": "aten::avg_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & avg_pool3d_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, ::std::optional<int64_t> divisor_override, at::Tensor & out); // {"schema": "aten::avg_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor avg_pool3d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, ::std::optional<int64_t> divisor_override); // {"schema": "aten::avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & avg_pool3d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, ::std::optional<int64_t> divisor_override, at::Tensor & grad_input); // {"schema": "aten::avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, ::std::optional<int64_t> divisor_override); // {"schema": "aten::avg_pool3d_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool2d_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples, at::Tensor & output, at::Tensor & indices); // {"schema": "aten::fractional_max_pool2d.output(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> fractional_max_pool2d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples); // {"schema": "aten::fractional_max_pool2d(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples) -> (Tensor, Tensor)", "dispatch": "True", "default": "True"}
at::Tensor & fractional_max_pool2d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices, at::Tensor & grad_input); // {"schema": "aten::fractional_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] output_size, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor fractional_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices); // {"schema": "aten::fractional_max_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] output_size, Tensor indices) -> Tensor", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool3d_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples, at::Tensor & output, at::Tensor & indices); // {"schema": "aten::fractional_max_pool3d.output(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> fractional_max_pool3d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples); // {"schema": "aten::fractional_max_pool3d(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples) -> (Tensor, Tensor)", "dispatch": "True", "default": "True"}
at::Tensor & fractional_max_pool3d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices, at::Tensor & grad_input); // {"schema": "aten::fractional_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] output_size, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor fractional_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices); // {"schema": "aten::fractional_max_pool3d_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] output_size, Tensor indices) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor &,at::Tensor &> max_pool2d_with_indices_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out, at::Tensor & indices); // {"schema": "aten::max_pool2d_with_indices.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> max_pool2d_with_indices(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode); // {"schema": "aten::max_pool2d_with_indices(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)", "dispatch": "True", "default": "True"}
at::Tensor & max_pool2d_with_indices_backward_out(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices, at::Tensor & grad_input); // {"schema": "aten::max_pool2d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor max_pool2d_with_indices_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices); // {"schema": "aten::max_pool2d_with_indices_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices) -> Tensor", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> max_pool3d_with_indices_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out, at::Tensor & indices); // {"schema": "aten::max_pool3d_with_indices.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> max_pool3d_with_indices(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode); // {"schema": "aten::max_pool3d_with_indices(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor & max_pool3d_with_indices_backward_out(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices, at::Tensor & grad_input); // {"schema": "aten::max_pool3d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor max_pool3d_with_indices_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices); // {"schema": "aten::max_pool3d_with_indices_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & max_unpool2d_out(const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::Tensor & out); // {"schema": "aten::max_unpool2d.out(Tensor self, Tensor indices, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor max_unpool2d(const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size); // {"schema": "aten::max_unpool2d(Tensor self, Tensor indices, SymInt[2] output_size) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & max_unpool3d_out(const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out); // {"schema": "aten::max_unpool3d.out(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor max_unpool3d(const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding); // {"schema": "aten::max_unpool3d(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & reflection_pad1d_out(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out); // {"schema": "aten::reflection_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor reflection_pad1d(const at::Tensor & self, c10::SymIntArrayRef padding); // {"schema": "aten::reflection_pad1d(Tensor self, SymInt[2] padding) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & reflection_pad1d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input); // {"schema": "aten::reflection_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor reflection_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding); // {"schema": "aten::reflection_pad1d_backward(Tensor grad_output, Tensor self, SymInt[2] padding) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & reflection_pad2d_out(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out); // {"schema": "aten::reflection_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor reflection_pad2d(const at::Tensor & self, c10::SymIntArrayRef padding); // {"schema": "aten::reflection_pad2d(Tensor self, SymInt[4] padding) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & reflection_pad2d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input); // {"schema": "aten::reflection_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor reflection_pad2d_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding); // {"schema": "aten::reflection_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & reflection_pad3d_out(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out); // {"schema": "aten::reflection_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor reflection_pad3d(const at::Tensor & self, c10::SymIntArrayRef padding); // {"schema": "aten::reflection_pad3d(Tensor self, SymInt[6] padding) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & reflection_pad3d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input); // {"schema": "aten::reflection_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor reflection_pad3d_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding); // {"schema": "aten::reflection_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & replication_pad1d_out(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out); // {"schema": "aten::replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor replication_pad1d(const at::Tensor & self, c10::SymIntArrayRef padding); // {"schema": "aten::replication_pad1d(Tensor self, SymInt[2] padding) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & replication_pad1d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input); // {"schema": "aten::replication_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor replication_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding); // {"schema": "aten::replication_pad1d_backward(Tensor grad_output, Tensor self, SymInt[2] padding) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & replication_pad2d_out(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out); // {"schema": "aten::replication_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor replication_pad2d(const at::Tensor & self, c10::SymIntArrayRef padding); // {"schema": "aten::replication_pad2d(Tensor self, SymInt[4] padding) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & replication_pad2d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input); // {"schema": "aten::replication_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor replication_pad2d_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding); // {"schema": "aten::replication_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & replication_pad3d_out(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out); // {"schema": "aten::replication_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor replication_pad3d(const at::Tensor & self, c10::SymIntArrayRef padding); // {"schema": "aten::replication_pad3d(Tensor self, SymInt[6] padding) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & replication_pad3d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input); // {"schema": "aten::replication_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor replication_pad3d_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding); // {"schema": "aten::replication_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _pad_circular(const at::Tensor & self, c10::SymIntArrayRef pad); // {"schema": "aten::_pad_circular(Tensor self, SymInt[] pad) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _pad_enum(const at::Tensor & self, c10::SymIntArrayRef pad, int64_t mode, ::std::optional<double> value); // {"schema": "aten::_pad_enum(Tensor self, SymInt[] pad, int mode, float? value=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor pad(const at::Tensor & self, c10::SymIntArrayRef pad, c10::string_view mode, ::std::optional<double> value); // {"schema": "aten::pad(Tensor self, SymInt[] pad, str mode=\"constant\", float? value=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor upsample_linear1d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors); // {"schema": "aten::upsample_linear1d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor upsample_bilinear2d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors); // {"schema": "aten::upsample_bilinear2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _upsample_bilinear2d_aa(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors); // {"schema": "aten::_upsample_bilinear2d_aa.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor upsample_trilinear3d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors); // {"schema": "aten::upsample_trilinear3d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor upsample_bicubic2d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors); // {"schema": "aten::upsample_bicubic2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _upsample_bicubic2d_aa(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors); // {"schema": "aten::_upsample_bicubic2d_aa.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _upsample_lanczos2d_aa(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors); // {"schema": "aten::_upsample_lanczos2d_aa.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor upsample_nearest1d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors); // {"schema": "aten::upsample_nearest1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _upsample_nearest_exact1d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors); // {"schema": "aten::_upsample_nearest_exact1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor upsample_nearest2d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors); // {"schema": "aten::upsample_nearest2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _upsample_nearest_exact2d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors); // {"schema": "aten::_upsample_nearest_exact2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor upsample_nearest3d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors); // {"schema": "aten::upsample_nearest3d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _upsample_nearest_exact3d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors); // {"schema": "aten::_upsample_nearest_exact3d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & upsample_linear1d_out(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales, at::Tensor & out); // {"schema": "aten::upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor upsample_linear1d(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales); // {"schema": "aten::upsample_linear1d(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & upsample_linear1d_backward_out(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales, at::Tensor & grad_input); // {"schema": "aten::upsample_linear1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor upsample_linear1d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales); // {"schema": "aten::upsample_linear1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & upsample_bilinear2d_out(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out); // {"schema": "aten::upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor upsample_bilinear2d(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::upsample_bilinear2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & upsample_bilinear2d_backward_out(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input); // {"schema": "aten::upsample_bilinear2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor upsample_bilinear2d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::upsample_bilinear2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _upsample_bilinear2d_aa_out(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out); // {"schema": "aten::_upsample_bilinear2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _upsample_bilinear2d_aa(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::_upsample_bilinear2d_aa(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _upsample_bilinear2d_aa_backward_out(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input); // {"schema": "aten::_upsample_bilinear2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _upsample_bilinear2d_aa_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::_upsample_bilinear2d_aa_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & upsample_bicubic2d_out(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out); // {"schema": "aten::upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor upsample_bicubic2d(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::upsample_bicubic2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & upsample_bicubic2d_backward_out(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input); // {"schema": "aten::upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor upsample_bicubic2d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::upsample_bicubic2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _upsample_bicubic2d_aa_out(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out); // {"schema": "aten::_upsample_bicubic2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _upsample_bicubic2d_aa(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::_upsample_bicubic2d_aa(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _upsample_bicubic2d_aa_backward_out(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input); // {"schema": "aten::_upsample_bicubic2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _upsample_bicubic2d_aa_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::_upsample_bicubic2d_aa_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _upsample_lanczos2d_aa_out(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out); // {"schema": "aten::_upsample_lanczos2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _upsample_lanczos2d_aa(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::_upsample_lanczos2d_aa(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _upsample_lanczos2d_aa_backward_out(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input); // {"schema": "aten::_upsample_lanczos2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _upsample_lanczos2d_aa_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::_upsample_lanczos2d_aa_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & upsample_trilinear3d_out(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out); // {"schema": "aten::upsample_trilinear3d.out(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor upsample_trilinear3d(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::upsample_trilinear3d(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & upsample_trilinear3d_backward_out(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input); // {"schema": "aten::upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor upsample_trilinear3d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::upsample_trilinear3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & upsample_nearest1d_out(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out); // {"schema": "aten::upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & _upsample_nearest_exact1d_out(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out); // {"schema": "aten::_upsample_nearest_exact1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor upsample_nearest1d(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales); // {"schema": "aten::upsample_nearest1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _upsample_nearest_exact1d(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales); // {"schema": "aten::_upsample_nearest_exact1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & upsample_nearest1d_backward_out(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input); // {"schema": "aten::upsample_nearest1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & _upsample_nearest_exact1d_backward_out(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input); // {"schema": "aten::_upsample_nearest_exact1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor upsample_nearest1d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales); // {"schema": "aten::upsample_nearest1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _upsample_nearest_exact1d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales); // {"schema": "aten::_upsample_nearest_exact1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & upsample_nearest2d_out(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out); // {"schema": "aten::upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & _upsample_nearest_exact2d_out(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out); // {"schema": "aten::_upsample_nearest_exact2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor upsample_nearest2d(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::upsample_nearest2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _upsample_nearest_exact2d(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::_upsample_nearest_exact2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & upsample_nearest2d_backward_out(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input); // {"schema": "aten::upsample_nearest2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & _upsample_nearest_exact2d_backward_out(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input); // {"schema": "aten::_upsample_nearest_exact2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor upsample_nearest2d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::upsample_nearest2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _upsample_nearest_exact2d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::_upsample_nearest_exact2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & upsample_nearest3d_out(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out); // {"schema": "aten::upsample_nearest3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & _upsample_nearest_exact3d_out(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out); // {"schema": "aten::_upsample_nearest_exact3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor upsample_nearest3d(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::upsample_nearest3d(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _upsample_nearest_exact3d(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::_upsample_nearest_exact3d(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & upsample_nearest3d_backward_out(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input); // {"schema": "aten::upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & _upsample_nearest_exact3d_backward_out(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input); // {"schema": "aten::_upsample_nearest_exact3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor upsample_nearest3d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::upsample_nearest3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _upsample_nearest_exact3d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w); // {"schema": "aten::_upsample_nearest_exact3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & sigmoid_backward_out(const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input); // {"schema": "aten::sigmoid_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & output); // {"schema": "aten::sigmoid_backward(Tensor grad_output, Tensor output) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & logit_backward_out(const at::Tensor & grad_output, const at::Tensor & self, ::std::optional<double> eps, at::Tensor & grad_input); // {"schema": "aten::logit_backward.grad_input(Tensor grad_output, Tensor self, float? eps=None, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor logit_backward(const at::Tensor & grad_output, const at::Tensor & self, ::std::optional<double> eps); // {"schema": "aten::logit_backward(Tensor grad_output, Tensor self, float? eps=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & tanh_backward_out(const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input); // {"schema": "aten::tanh_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor tanh_backward(const at::Tensor & grad_output, const at::Tensor & output); // {"schema": "aten::tanh_backward(Tensor grad_output, Tensor output) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & slow_conv_transpose2d_out(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef dilation, at::Tensor & out); // {"schema": "aten::slow_conv_transpose2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor slow_conv_transpose2d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef dilation); // {"schema": "aten::slow_conv_transpose2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt[2] dilation=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & slow_conv_transpose3d_out(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef dilation, at::Tensor & out); // {"schema": "aten::slow_conv_transpose3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor slow_conv_transpose3d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef dilation); // {"schema": "aten::slow_conv_transpose3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt[3] dilation=1) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & thnn_conv2d_out(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & out); // {"schema": "aten::thnn_conv2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor thnn_conv2d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding); // {"schema": "aten::thnn_conv2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & _slow_conv2d_forward_out(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & output); // {"schema": "aten::_slow_conv2d_forward.output(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) output) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _slow_conv2d_forward(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding); // {"schema": "aten::_slow_conv2d_forward(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias); // {"schema": "aten::_slow_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _slow_conv2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, ::std::array<bool,3> output_mask); // {"schema": "aten::_slow_conv2d_backward.output_mask(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)", "dispatch": "True", "default": "False"}
at::Tensor & _conv_depthwise2d_out(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out); // {"schema": "aten::_conv_depthwise2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _conv_depthwise2d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation); // {"schema": "aten::_conv_depthwise2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor conv_depthwise3d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation); // {"schema": "aten::conv_depthwise3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & slow_conv3d_out(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & out); // {"schema": "aten::slow_conv3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor slow_conv3d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding); // {"schema": "aten::slow_conv3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & slow_conv3d_forward_out(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & output); // {"schema": "aten::slow_conv3d_forward.output(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, *, Tensor(a!) output) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor slow_conv3d_forward(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding); // {"schema": "aten::slow_conv3d_forward(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor slow_conv_dilated2d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation); // {"schema": "aten::slow_conv_dilated2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor slow_conv_dilated3d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation); // {"schema": "aten::slow_conv_dilated3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & col2im_out(const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out); // {"schema": "aten::col2im.out(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor col2im(const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride); // {"schema": "aten::col2im(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor column_stack(at::TensorList tensors); // {"schema": "aten::column_stack(Tensor[] tensors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & column_stack_out(at::TensorList tensors, at::Tensor & out); // {"schema": "aten::column_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & im2col_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out); // {"schema": "aten::im2col.out(Tensor self, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor im2col(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride); // {"schema": "aten::im2col(Tensor self, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor isfinite(const at::Tensor & self); // {"schema": "aten::isfinite(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor isinf(const at::Tensor & self); // {"schema": "aten::isinf(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
void record_stream(at::Tensor & self, at::Stream s); // {"schema": "aten::record_stream(Tensor(a!) self, Stream s) -> ()", "dispatch": "True", "default": "False"}
at::Tensor isposinf(const at::Tensor & self); // {"schema": "aten::isposinf(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & isposinf_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::isposinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor isneginf(const at::Tensor & self); // {"schema": "aten::isneginf(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & isneginf_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::isneginf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _add_batch_dim(const at::Tensor & self, int64_t batch_dim, int64_t level); // {"schema": "aten::_add_batch_dim(Tensor self, int batch_dim, int level) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _remove_batch_dim(const at::Tensor & self, int64_t level, c10::SymInt batch_size, int64_t out_dim); // {"schema": "aten::_remove_batch_dim(Tensor self, int level, SymInt batch_size, int out_dim) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor special_entr(const at::Tensor & self); // {"schema": "aten::special_entr(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_entr_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_entr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_ndtri(const at::Tensor & self); // {"schema": "aten::special_ndtri(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_ndtri_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_ndtri.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_log_ndtr(const at::Tensor & self); // {"schema": "aten::special_log_ndtr(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_log_ndtr_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_log_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_expm1(const at::Tensor & self); // {"schema": "aten::special_expm1(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_expm1_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_exp2(const at::Tensor & self); // {"schema": "aten::special_exp2(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_exp2_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_psi(const at::Tensor & self); // {"schema": "aten::special_psi(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_psi_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_psi.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_digamma(const at::Tensor & self); // {"schema": "aten::special_digamma(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_digamma_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_gammaln(const at::Tensor & self); // {"schema": "aten::special_gammaln(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_gammaln_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_gammaln.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_erf(const at::Tensor & self); // {"schema": "aten::special_erf(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_erf_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_erfc(const at::Tensor & self); // {"schema": "aten::special_erfc(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_erfc_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_erfcx(const at::Tensor & self); // {"schema": "aten::special_erfcx(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_erfcx_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_erfcx.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_erfinv(const at::Tensor & self); // {"schema": "aten::special_erfinv(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_erfinv_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_ndtr(const at::Tensor & self); // {"schema": "aten::special_ndtr(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_ndtr_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_xlog1py(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::special_xlog1py(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_xlog1py(const at::Scalar & self, const at::Tensor & other); // {"schema": "aten::special_xlog1py.self_scalar(Scalar self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_xlog1py(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::special_xlog1py.other_scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_xlog1py_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::special_xlog1py.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & special_xlog1py_out(const at::Scalar & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::special_xlog1py.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & special_xlog1py_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::special_xlog1py.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor special_xlogy(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::special_xlogy(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor special_xlogy(const at::Scalar & self, const at::Tensor & other); // {"schema": "aten::special_xlogy.self_scalar(Scalar self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor special_xlogy(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::special_xlogy.other_scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_xlogy_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::special_xlogy.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & special_xlogy_out(const at::Scalar & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::special_xlogy.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & special_xlogy_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::special_xlogy.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_zeta(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::special_zeta(Tensor self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_zeta(const at::Scalar & self, const at::Tensor & other); // {"schema": "aten::special_zeta.self_scalar(Scalar self, Tensor other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_zeta(const at::Tensor & self, const at::Scalar & other); // {"schema": "aten::special_zeta.other_scalar(Tensor self, Scalar other) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_zeta_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::special_zeta.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & special_zeta_out(const at::Scalar & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::special_zeta.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & special_zeta_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::special_zeta.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor special_i0(const at::Tensor & self); // {"schema": "aten::special_i0(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_i0_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_i0e(const at::Tensor & self); // {"schema": "aten::special_i0e(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_i0e_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_i0e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_i1(const at::Tensor & self); // {"schema": "aten::special_i1(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_i1_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_i1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_i1e(const at::Tensor & self); // {"schema": "aten::special_i1e(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_i1e_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_i1e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_logit(const at::Tensor & self, ::std::optional<double> eps); // {"schema": "aten::special_logit(Tensor self, float? eps=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_logit_out(const at::Tensor & self, ::std::optional<double> eps, at::Tensor & out); // {"schema": "aten::special_logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_polygamma(int64_t n, const at::Tensor & self); // {"schema": "aten::special_polygamma(int n, Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_polygamma_out(int64_t n, const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_logsumexp(const at::Tensor & self, at::IntArrayRef dim, bool keepdim); // {"schema": "aten::special_logsumexp(Tensor self, int[1] dim, bool keepdim=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_logsumexp_out(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out); // {"schema": "aten::special_logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_expit(const at::Tensor & self); // {"schema": "aten::special_expit(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_expit_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_expit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_sinc(const at::Tensor & self); // {"schema": "aten::special_sinc(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_sinc_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_round(const at::Tensor & self, int64_t decimals); // {"schema": "aten::special_round(Tensor self, *, int decimals=0) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_round_out(const at::Tensor & self, int64_t decimals, at::Tensor & out); // {"schema": "aten::special_round.out(Tensor self, *, int decimals=0, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_log1p(const at::Tensor & self); // {"schema": "aten::special_log1p(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_log1p_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_log_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::special_log_softmax(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_gammainc_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::special_gammainc.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_gammainc(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::special_gammainc(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_gammaincc_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::special_gammaincc.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_gammaincc(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::special_gammaincc(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor special_multigammaln(const at::Tensor & self, int64_t p); // {"schema": "aten::special_multigammaln(Tensor self, int p) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & special_multigammaln_out(const at::Tensor & self, int64_t p, at::Tensor & out); // {"schema": "aten::special_multigammaln.out(Tensor self, int p, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor special_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::special_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor fft_fft(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_fft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_fft_out(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_fft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_ifft(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_ifft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_ifft_out(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_ifft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_rfft(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_rfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_rfft_out(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_rfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_irfft(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_irfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_irfft_out(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_irfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_hfft(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_hfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_hfft_out(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_hfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_ihfft(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_ihfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_ihfft_out(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_ihfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_fft2(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_fft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_fft2_out(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_fft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_ifft2(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_ifft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_ifft2_out(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_ifft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_rfft2(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_rfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_rfft2_out(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_rfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_irfft2(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_irfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_irfft2_out(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_irfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_hfft2(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_hfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_hfft2_out(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_hfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_ihfft2(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_ihfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_ihfft2_out(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_ihfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_fftn(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_fftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_fftn_out(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_fftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_ifftn(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_ifftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_ifftn_out(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_ifftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_rfftn(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_rfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_rfftn_out(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_rfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_irfftn(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_irfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_irfftn_out(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_irfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_hfftn(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_hfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_hfftn_out(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_hfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_ihfftn(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm); // {"schema": "aten::fft_ihfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & fft_ihfftn_out(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out); // {"schema": "aten::fft_ihfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor fft_fftfreq(int64_t n, double d, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::fft_fftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & fft_fftfreq_out(int64_t n, double d, at::Tensor & out); // {"schema": "aten::fft_fftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor fft_rfftfreq(int64_t n, double d, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::fft_rfftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & fft_rfftfreq_out(int64_t n, double d, at::Tensor & out); // {"schema": "aten::fft_rfftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor fft_fftshift(const at::Tensor & self, at::OptionalIntArrayRef dim); // {"schema": "aten::fft_fftshift(Tensor self, int[1]? dim=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor fft_ifftshift(const at::Tensor & self, at::OptionalIntArrayRef dim); // {"schema": "aten::fft_ifftshift(Tensor self, int[1]? dim=None) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> linalg_cholesky_ex(const at::Tensor & self, bool upper, bool check_errors); // {"schema": "aten::linalg_cholesky_ex(Tensor self, *, bool upper=False, bool check_errors=False) -> (Tensor L, Tensor info)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> linalg_cholesky_ex_out(const at::Tensor & self, bool upper, bool check_errors, at::Tensor & L, at::Tensor & info); // {"schema": "aten::linalg_cholesky_ex.L(Tensor self, *, bool upper=False, bool check_errors=False, Tensor(a!) L, Tensor(b!) info) -> (Tensor(a!) L, Tensor(b!) info)", "dispatch": "True", "default": "False"}
at::Tensor linalg_cholesky(const at::Tensor & self, bool upper); // {"schema": "aten::linalg_cholesky(Tensor self, *, bool upper=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_cholesky_out(const at::Tensor & self, bool upper, at::Tensor & out); // {"schema": "aten::linalg_cholesky.out(Tensor self, *, bool upper=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_cross(const at::Tensor & self, const at::Tensor & other, int64_t dim); // {"schema": "aten::linalg_cross(Tensor self, Tensor other, *, int dim=-1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & linalg_cross_out(const at::Tensor & self, const at::Tensor & other, int64_t dim, at::Tensor & out); // {"schema": "aten::linalg_cross.out(Tensor self, Tensor other, *, int dim=-1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> linalg_lu_factor(const at::Tensor & A, bool pivot); // {"schema": "aten::linalg_lu_factor(Tensor A, *, bool pivot=True) -> (Tensor LU, Tensor pivots)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> linalg_lu_factor_out(const at::Tensor & A, bool pivot, at::Tensor & LU, at::Tensor & pivots); // {"schema": "aten::linalg_lu_factor.out(Tensor A, *, bool pivot=True, Tensor(a!) LU, Tensor(b!) pivots) -> (Tensor(a!) LU, Tensor(b!) pivots)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_lu_factor_ex(const at::Tensor & A, bool pivot, bool check_errors); // {"schema": "aten::linalg_lu_factor_ex(Tensor A, *, bool pivot=True, bool check_errors=False) -> (Tensor LU, Tensor pivots, Tensor info)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_lu_factor_ex_out(const at::Tensor & A, bool pivot, bool check_errors, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info); // {"schema": "aten::linalg_lu_factor_ex.out(Tensor A, *, bool pivot=True, bool check_errors=False, Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info) -> (Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_lu(const at::Tensor & A, bool pivot); // {"schema": "aten::linalg_lu(Tensor A, *, bool pivot=True) -> (Tensor P, Tensor L, Tensor U)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_lu_out(const at::Tensor & A, bool pivot, at::Tensor & P, at::Tensor & L, at::Tensor & U); // {"schema": "aten::linalg_lu.out(Tensor A, *, bool pivot=True, Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) -> (Tensor(a!) P, Tensor(b!) L, Tensor(c!) U)", "dispatch": "True", "default": "False"}
at::Tensor linalg_lu_solve(const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left, bool adjoint); // {"schema": "aten::linalg_lu_solve(Tensor LU, Tensor pivots, Tensor B, *, bool left=True, bool adjoint=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & linalg_lu_solve_out(const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left, bool adjoint, at::Tensor & out); // {"schema": "aten::linalg_lu_solve.out(Tensor LU, Tensor pivots, Tensor B, *, bool left=True, bool adjoint=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _linalg_det(const at::Tensor & A); // {"schema": "aten::_linalg_det(Tensor A) -> (Tensor result, Tensor LU, Tensor pivots)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _linalg_det_out(const at::Tensor & A, at::Tensor & result, at::Tensor & LU, at::Tensor & pivots); // {"schema": "aten::_linalg_det.result(Tensor A, *, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots)", "dispatch": "True", "default": "False"}
at::Tensor linalg_det(const at::Tensor & A); // {"schema": "aten::linalg_det(Tensor A) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_det_out(const at::Tensor & A, at::Tensor & out); // {"schema": "aten::linalg_det.out(Tensor A, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor det(const at::Tensor & self); // {"schema": "aten::det(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_ldl_factor_ex(const at::Tensor & self, bool hermitian, bool check_errors); // {"schema": "aten::linalg_ldl_factor_ex(Tensor self, *, bool hermitian=False, bool check_errors=False) -> (Tensor LD, Tensor pivots, Tensor info)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_ldl_factor_ex_out(const at::Tensor & self, bool hermitian, bool check_errors, at::Tensor & LD, at::Tensor & pivots, at::Tensor & info); // {"schema": "aten::linalg_ldl_factor_ex.out(Tensor self, *, bool hermitian=False, bool check_errors=False, Tensor(a!) LD, Tensor(b!) pivots, Tensor(c!) info) -> (Tensor(a!) LD, Tensor(b!) pivots, Tensor(c!) info)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> linalg_ldl_factor(const at::Tensor & self, bool hermitian); // {"schema": "aten::linalg_ldl_factor(Tensor self, *, bool hermitian=False) -> (Tensor LD, Tensor pivots)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> linalg_ldl_factor_out(const at::Tensor & self, bool hermitian, at::Tensor & LD, at::Tensor & pivots); // {"schema": "aten::linalg_ldl_factor.out(Tensor self, *, bool hermitian=False, Tensor(a!) LD, Tensor(b!) pivots) -> (Tensor(a!) LD, Tensor(b!) pivots)", "dispatch": "False", "default": "True"}
at::Tensor linalg_ldl_solve(const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian); // {"schema": "aten::linalg_ldl_solve(Tensor LD, Tensor pivots, Tensor B, *, bool hermitian=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & linalg_ldl_solve_out(const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian, at::Tensor & out); // {"schema": "aten::linalg_ldl_solve.out(Tensor LD, Tensor pivots, Tensor B, *, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> linalg_lstsq(const at::Tensor & self, const at::Tensor & b, ::std::optional<double> rcond, ::std::optional<c10::string_view> driver); // {"schema": "aten::linalg_lstsq(Tensor self, Tensor b, float? rcond=None, *, str? driver=None) -> (Tensor solution, Tensor residuals, Tensor rank, Tensor singular_values)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> linalg_lstsq_out(const at::Tensor & self, const at::Tensor & b, ::std::optional<double> rcond, ::std::optional<c10::string_view> driver, at::Tensor & solution, at::Tensor & residuals, at::Tensor & rank, at::Tensor & singular_values); // {"schema": "aten::linalg_lstsq.out(Tensor self, Tensor b, float? rcond=None, *, str? driver=None, Tensor(a!) solution, Tensor(b!) residuals, Tensor(c!) rank, Tensor(d!) singular_values) -> (Tensor(a!) solution, Tensor(b!) residuals, Tensor(c!) rank, Tensor(d!) singular_values)", "dispatch": "True", "default": "False"}
at::Tensor linalg_matmul(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::linalg_matmul(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_matmul_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::linalg_matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_vecdot(const at::Tensor & x, const at::Tensor & y, int64_t dim); // {"schema": "aten::linalg_vecdot(Tensor x, Tensor y, *, int dim=-1) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_vecdot_out(const at::Tensor & x, const at::Tensor & y, int64_t dim, at::Tensor & out); // {"schema": "aten::linalg_vecdot.out(Tensor x, Tensor y, *, int dim=-1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_matrix_exp(const at::Tensor & self); // {"schema": "aten::linalg_matrix_exp(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _linalg_slogdet(const at::Tensor & A); // {"schema": "aten::_linalg_slogdet(Tensor A) -> (Tensor sign, Tensor logabsdet, Tensor LU, Tensor pivots)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _linalg_slogdet_out(const at::Tensor & A, at::Tensor & sign, at::Tensor & logabsdet, at::Tensor & LU, at::Tensor & pivots); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> linalg_slogdet(const at::Tensor & A); // {"schema": "aten::linalg_slogdet(Tensor A) -> (Tensor sign, Tensor logabsdet)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> linalg_slogdet_out(const at::Tensor & A, at::Tensor & sign, at::Tensor & logabsdet); // {"schema": "aten::linalg_slogdet.out(Tensor A, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> slogdet(const at::Tensor & self); // {"schema": "aten::slogdet(Tensor self) -> (Tensor sign, Tensor logabsdet)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> slogdet_out(const at::Tensor & self, at::Tensor & sign, at::Tensor & logabsdet); // {"schema": "aten::slogdet.out(Tensor self, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet)", "dispatch": "False", "default": "True"}
at::Tensor logdet(const at::Tensor & self); // {"schema": "aten::logdet(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> linalg_eig(const at::Tensor & self); // {"schema": "aten::linalg_eig(Tensor self) -> (Tensor eigenvalues, Tensor eigenvectors)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor &,at::Tensor &> linalg_eig_out(const at::Tensor & self, at::Tensor & eigenvalues, at::Tensor & eigenvectors); // {"schema": "aten::linalg_eig.out(Tensor self, *, Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)", "dispatch": "True", "default": "False"}
at::Tensor _linalg_eigvals(const at::Tensor & self); // {"schema": "aten::_linalg_eigvals(Tensor self) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor linalg_eigvals(const at::Tensor & self); // {"schema": "aten::linalg_eigvals(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_eigvals_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::linalg_eigvals.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _linalg_eigh(const at::Tensor & A, c10::string_view UPLO, bool compute_v); // {"schema": "aten::_linalg_eigh(Tensor A, str UPLO=\"L\", bool compute_v=True) -> (Tensor eigenvalues, Tensor eigenvectors)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> _linalg_eigh_out(const at::Tensor & A, c10::string_view UPLO, bool compute_v, at::Tensor & eigenvalues, at::Tensor & eigenvectors); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> linalg_eigh(const at::Tensor & self, c10::string_view UPLO); // {"schema": "aten::linalg_eigh(Tensor self, str UPLO=\"L\") -> (Tensor eigenvalues, Tensor eigenvectors)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> linalg_eigh_out(const at::Tensor & self, c10::string_view UPLO, at::Tensor & eigvals, at::Tensor & eigvecs); // {"schema": "aten::linalg_eigh.eigvals(Tensor self, str UPLO=\"L\", *, Tensor(a!) eigvals, Tensor(b!) eigvecs) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)", "dispatch": "False", "default": "True"}
at::Tensor linalg_eigvalsh(const at::Tensor & self, c10::string_view UPLO); // {"schema": "aten::linalg_eigvalsh(Tensor self, str UPLO=\"L\") -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_eigvalsh_out(const at::Tensor & self, c10::string_view UPLO, at::Tensor & out); // {"schema": "aten::linalg_eigvalsh.out(Tensor self, str UPLO=\"L\", *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_householder_product(const at::Tensor & input, const at::Tensor & tau); // {"schema": "aten::linalg_householder_product(Tensor input, Tensor tau) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & linalg_householder_product_out(const at::Tensor & input, const at::Tensor & tau, at::Tensor & out); // {"schema": "aten::linalg_householder_product.out(Tensor input, Tensor tau, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> linalg_inv_ex(const at::Tensor & A, bool check_errors); // {"schema": "aten::linalg_inv_ex(Tensor A, *, bool check_errors=False) -> (Tensor inverse, Tensor info)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> linalg_inv_ex_out(const at::Tensor & A, bool check_errors, at::Tensor & inverse, at::Tensor & info); // {"schema": "aten::linalg_inv_ex.inverse(Tensor A, *, bool check_errors=False, Tensor(a!) inverse, Tensor(b!) info) -> (Tensor(a!) inverse, Tensor(b!) info)", "dispatch": "True", "default": "False"}
at::Tensor linalg_inv(const at::Tensor & A); // {"schema": "aten::linalg_inv(Tensor A) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_inv_out(const at::Tensor & A, at::Tensor & out); // {"schema": "aten::linalg_inv.out(Tensor A, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor inverse(const at::Tensor & self); // {"schema": "aten::inverse(Tensor self) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & inverse_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor inner(const at::Tensor & self, const at::Tensor & other); // {"schema": "aten::inner(Tensor self, Tensor other) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & inner_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::inner.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor outer(const at::Tensor & self, const at::Tensor & vec2); // {"schema": "aten::outer(Tensor self, Tensor vec2) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & outer_out(const at::Tensor & self, const at::Tensor & vec2, at::Tensor & out); // {"schema": "aten::outer.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor ger(const at::Tensor & self, const at::Tensor & vec2); // {"schema": "aten::ger(Tensor self, Tensor vec2) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & ger_out(const at::Tensor & self, const at::Tensor & vec2, at::Tensor & out); // {"schema": "aten::ger.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_norm(const at::Tensor & self, const ::std::optional<at::Scalar> & ord, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::linalg_norm(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor linalg_norm(const at::Tensor & self, c10::string_view ord, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::linalg_norm.ord_str(Tensor self, str ord, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_norm_out(const at::Tensor & self, const ::std::optional<at::Scalar> & ord, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::linalg_norm.out(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & linalg_norm_out(const at::Tensor & self, c10::string_view ord, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::linalg_norm.ord_str_out(Tensor self, str ord, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_vector_norm(const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::linalg_vector_norm(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & linalg_vector_norm_out(const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::linalg_vector_norm.out(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor linalg__powsum(const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::linalg__powsum(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor linalg_matrix_norm(const at::Tensor & self, const at::Scalar & ord, at::IntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::linalg_matrix_norm(Tensor self, Scalar ord, int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_matrix_norm_out(const at::Tensor & self, const at::Scalar & ord, at::IntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::linalg_matrix_norm.out(Tensor self, Scalar ord, int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_matrix_norm(const at::Tensor & self, c10::string_view ord, at::IntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::linalg_matrix_norm.str_ord(Tensor self, str ord='fro', int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_matrix_norm_out(const at::Tensor & self, c10::string_view ord, at::IntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::linalg_matrix_norm.str_ord_out(Tensor self, str ord='fro', int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _linalg_svd(const at::Tensor & A, bool full_matrices, bool compute_uv, ::std::optional<c10::string_view> driver); // {"schema": "aten::_linalg_svd(Tensor A, bool full_matrices=False, bool compute_uv=True, *, str? driver=None) -> (Tensor U, Tensor S, Tensor Vh)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _linalg_svd_out(const at::Tensor & A, bool full_matrices, bool compute_uv, ::std::optional<c10::string_view> driver, at::Tensor & U, at::Tensor & S, at::Tensor & Vh); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_svd(const at::Tensor & A, bool full_matrices, ::std::optional<c10::string_view> driver); // {"schema": "aten::linalg_svd(Tensor A, bool full_matrices=True, *, str? driver=None) -> (Tensor U, Tensor S, Tensor Vh)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_svd_out(const at::Tensor & A, bool full_matrices, ::std::optional<c10::string_view> driver, at::Tensor & U, at::Tensor & S, at::Tensor & Vh); // {"schema": "aten::linalg_svd.U(Tensor A, bool full_matrices=True, *, str? driver=None, Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh)", "dispatch": "False", "default": "True"}
at::Tensor linalg_svdvals(const at::Tensor & A, ::std::optional<c10::string_view> driver); // {"schema": "aten::linalg_svdvals(Tensor A, *, str? driver=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_svdvals_out(const at::Tensor & A, ::std::optional<c10::string_view> driver, at::Tensor & out); // {"schema": "aten::linalg_svdvals.out(Tensor A, *, str? driver=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_cond(const at::Tensor & self, const ::std::optional<at::Scalar> & p); // {"schema": "aten::linalg_cond(Tensor self, Scalar? p=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_cond_out(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::Tensor & out); // {"schema": "aten::linalg_cond.out(Tensor self, Scalar? p=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_cond(const at::Tensor & self, c10::string_view p); // {"schema": "aten::linalg_cond.p_str(Tensor self, str p) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_cond_out(const at::Tensor & self, c10::string_view p, at::Tensor & out); // {"schema": "aten::linalg_cond.p_str_out(Tensor self, str p, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_pinv(const at::Tensor & self, const ::std::optional<at::Tensor> & atol, const ::std::optional<at::Tensor> & rtol, bool hermitian); // {"schema": "aten::linalg_pinv.atol_rtol_tensor(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & linalg_pinv_out(const at::Tensor & self, const ::std::optional<at::Tensor> & atol, const ::std::optional<at::Tensor> & rtol, bool hermitian, at::Tensor & out); // {"schema": "aten::linalg_pinv.atol_rtol_tensor_out(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor linalg_pinv(const at::Tensor & self, ::std::optional<double> atol, ::std::optional<double> rtol, bool hermitian); // {"schema": "aten::linalg_pinv.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_pinv_out(const at::Tensor & self, ::std::optional<double> atol, ::std::optional<double> rtol, bool hermitian, at::Tensor & out); // {"schema": "aten::linalg_pinv.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_pinv(const at::Tensor & self, double rcond, bool hermitian); // {"schema": "aten::linalg_pinv(Tensor self, float rcond, bool hermitian=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor linalg_pinv(const at::Tensor & self, const at::Tensor & rcond, bool hermitian); // {"schema": "aten::linalg_pinv.rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_pinv_out(const at::Tensor & self, double rcond, bool hermitian, at::Tensor & out); // {"schema": "aten::linalg_pinv.out(Tensor self, float rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor & linalg_pinv_out(const at::Tensor & self, const at::Tensor & rcond, bool hermitian, at::Tensor & out); // {"schema": "aten::linalg_pinv.out_rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _linalg_solve_ex(const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors); // {"schema": "aten::_linalg_solve_ex(Tensor A, Tensor B, *, bool left=True, bool check_errors=False) -> (Tensor result, Tensor LU, Tensor pivots, Tensor info)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _linalg_solve_ex_out(const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors, at::Tensor & result, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> linalg_solve_ex(const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors); // {"schema": "aten::linalg_solve_ex(Tensor A, Tensor B, *, bool left=True, bool check_errors=False) -> (Tensor result, Tensor info)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> linalg_solve_ex_out(const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors, at::Tensor & result, at::Tensor & info); // {"schema": "aten::linalg_solve_ex.out(Tensor A, Tensor B, *, bool left=True, bool check_errors=False, Tensor(a!) result, Tensor(b!) info) -> (Tensor(a!) result, Tensor(b!) info)", "dispatch": "False", "default": "True"}
at::Tensor linalg_solve(const at::Tensor & A, const at::Tensor & B, bool left); // {"schema": "aten::linalg_solve(Tensor A, Tensor B, *, bool left=True) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _spsolve(const at::Tensor & A, const at::Tensor & B, bool left); // {"schema": "aten::_spsolve(Tensor A, Tensor B, *, bool left=True) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & linalg_solve_out(const at::Tensor & A, const at::Tensor & B, bool left, at::Tensor & out); // {"schema": "aten::linalg_solve.out(Tensor A, Tensor B, *, bool left=True, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_tensorinv(const at::Tensor & self, int64_t ind); // {"schema": "aten::linalg_tensorinv(Tensor self, int ind=2) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_tensorinv_out(const at::Tensor & self, int64_t ind, at::Tensor & out); // {"schema": "aten::linalg_tensorinv.out(Tensor self, int ind=2, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_tensorsolve(const at::Tensor & self, const at::Tensor & other, at::OptionalIntArrayRef dims); // {"schema": "aten::linalg_tensorsolve(Tensor self, Tensor other, int[]? dims=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_tensorsolve_out(const at::Tensor & self, const at::Tensor & other, at::OptionalIntArrayRef dims, at::Tensor & out); // {"schema": "aten::linalg_tensorsolve.out(Tensor self, Tensor other, int[]? dims=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> linalg_qr(const at::Tensor & A, c10::string_view mode); // {"schema": "aten::linalg_qr(Tensor A, str mode='reduced') -> (Tensor Q, Tensor R)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> linalg_qr_out(const at::Tensor & A, c10::string_view mode, at::Tensor & Q, at::Tensor & R); // {"schema": "aten::linalg_qr.out(Tensor A, str mode='reduced', *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R)", "dispatch": "True", "default": "False"}
at::Tensor linalg_matrix_power(const at::Tensor & self, int64_t n); // {"schema": "aten::linalg_matrix_power(Tensor self, int n) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_matrix_power_out(const at::Tensor & self, int64_t n, at::Tensor & out); // {"schema": "aten::linalg_matrix_power.out(Tensor self, int n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_matrix_rank(const at::Tensor & input, const ::std::optional<at::Tensor> & atol, const ::std::optional<at::Tensor> & rtol, bool hermitian); // {"schema": "aten::linalg_matrix_rank.atol_rtol_tensor(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_matrix_rank_out(const at::Tensor & input, const ::std::optional<at::Tensor> & atol, const ::std::optional<at::Tensor> & rtol, bool hermitian, at::Tensor & out); // {"schema": "aten::linalg_matrix_rank.atol_rtol_tensor_out(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_matrix_rank(const at::Tensor & self, ::std::optional<double> atol, ::std::optional<double> rtol, bool hermitian); // {"schema": "aten::linalg_matrix_rank.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_matrix_rank_out(const at::Tensor & self, ::std::optional<double> atol, ::std::optional<double> rtol, bool hermitian, at::Tensor & out); // {"schema": "aten::linalg_matrix_rank.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_matrix_rank(const at::Tensor & self, double tol, bool hermitian); // {"schema": "aten::linalg_matrix_rank(Tensor self, float tol, bool hermitian=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_matrix_rank_out(const at::Tensor & self, double tol, bool hermitian, at::Tensor & out); // {"schema": "aten::linalg_matrix_rank.out(Tensor self, float tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_matrix_rank(const at::Tensor & input, const at::Tensor & tol, bool hermitian); // {"schema": "aten::linalg_matrix_rank.tol_tensor(Tensor input, Tensor tol, bool hermitian=False) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_matrix_rank_out(const at::Tensor & input, const at::Tensor & tol, bool hermitian, at::Tensor & out); // {"schema": "aten::linalg_matrix_rank.out_tol_tensor(Tensor input, Tensor tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor linalg_multi_dot(at::TensorList tensors); // {"schema": "aten::linalg_multi_dot(Tensor[] tensors) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor & linalg_multi_dot_out(at::TensorList tensors, at::Tensor & out); // {"schema": "aten::linalg_multi_dot.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "False", "default": "True"}
at::Tensor nested_to_padded_tensor(const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size); // {"schema": "aten::nested_to_padded_tensor(Tensor self, float padding, int[]? output_size=None) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _test_serialization_subcmul(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha); // {"schema": "aten::_test_serialization_subcmul(Tensor self, Tensor other, Scalar alpha=1) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _test_parallel_materialize(const at::Tensor & self, int64_t num_parallel, bool skip_first); // {"schema": "aten::_test_parallel_materialize(Tensor self, int num_parallel, bool skip_first=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _test_optional_intlist(const at::Tensor & values, at::OptionalIntArrayRef addends); // {"schema": "aten::_test_optional_intlist(Tensor values, int[]? addends) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _test_optional_filled_intlist(const at::Tensor & values, at::OptionalIntArrayRef addends); // {"schema": "aten::_test_optional_filled_intlist(Tensor values, int[2]? addends) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _test_optional_floatlist(const at::Tensor & values, ::std::optional<at::ArrayRef<double>> addends); // {"schema": "aten::_test_optional_floatlist(Tensor values, float[]? addends) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _test_string_default(const at::Tensor & dummy, c10::string_view a, c10::string_view b); // {"schema": "aten::_test_string_default(Tensor dummy, str a=\"\\\"'\\\\\", str b='\"\\'\\\\') -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _test_ambiguous_defaults(const at::Tensor & dummy, int64_t a, int64_t b); // {"schema": "aten::_test_ambiguous_defaults.a(Tensor dummy, int a=1, int b=1) -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _test_ambiguous_defaults(const at::Tensor & dummy, int64_t a, c10::string_view b); // {"schema": "aten::_test_ambiguous_defaults.b(Tensor dummy, int a=2, str b=\"2\") -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor _test_warn_in_autograd(const at::Tensor & self); // {"schema": "aten::_test_warn_in_autograd(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _test_autograd_multiple_dispatch(const at::Tensor & self); // {"schema": "aten::_test_autograd_multiple_dispatch.fullcoverage(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _test_autograd_multiple_dispatch(const at::Tensor & self, bool b); // {"schema": "aten::_test_autograd_multiple_dispatch.ntonly(Tensor self, bool b) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _test_autograd_multiple_dispatch_view(const at::Tensor & self); // {"schema": "aten::_test_autograd_multiple_dispatch_view(Tensor(a) self) -> Tensor(a)", "dispatch": "True", "default": "True"}
at::Tensor _test_autograd_multiple_dispatch_view_copy(const at::Tensor & self); // {"schema": "aten::_test_autograd_multiple_dispatch_view_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor segment_reduce(const at::Tensor & data, c10::string_view reduce, const ::std::optional<at::Tensor> & lengths, const ::std::optional<at::Tensor> & indices, const ::std::optional<at::Tensor> & offsets, int64_t axis, bool unsafe, const ::std::optional<at::Scalar> & initial); // {"schema": "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", "dispatch": "True", "default": "False"}
at::Tensor _segment_reduce_backward(const at::Tensor & grad, const at::Tensor & output, const at::Tensor & data, c10::string_view reduce, const ::std::optional<at::Tensor> & lengths, const ::std::optional<at::Tensor> & offsets, int64_t axis, const ::std::optional<at::Scalar> & initial); // {"schema": "aten::_segment_reduce_backward(Tensor grad, Tensor output, Tensor data, str reduce, *, Tensor? lengths=None, Tensor? offsets=None, int axis=0, Scalar? initial=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor pad_sequence(at::TensorList sequences, bool batch_first, double padding_value, c10::string_view padding_side); // {"schema": "aten::pad_sequence(Tensor[] sequences, bool batch_first=False, float padding_value=0.0, str padding_side=\"right\") -> Tensor", "dispatch": "False", "default": "True"}
at::Tensor flatten_dense_tensors(at::TensorList tensors); // {"schema": "aten::flatten_dense_tensors(Tensor[] tensors) -> Tensor", "dispatch": "False", "default": "True"}
::std::vector<at::Tensor> unflatten_dense_tensors(const at::Tensor & flat, at::TensorList tensors); // {"schema": "aten::unflatten_dense_tensors(Tensor flat, Tensor[] tensors) -> Tensor[]", "dispatch": "False", "default": "True"}
at::Tensor _nested_tensor_from_tensor_list(at::TensorList list, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory); // {"schema": "aten::_nested_tensor_from_tensor_list(Tensor[] list, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _fw_primal_copy(const at::Tensor & self, int64_t level); // {"schema": "aten::_fw_primal_copy(Tensor self, int level) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _make_dual_copy(const at::Tensor & primal, const at::Tensor & tangent, int64_t level); // {"schema": "aten::_make_dual_copy(Tensor primal, Tensor tangent, int level) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor view_as_real_copy(const at::Tensor & self); // {"schema": "aten::view_as_real_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor view_as_complex_copy(const at::Tensor & self); // {"schema": "aten::view_as_complex_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _conj_copy(const at::Tensor & self); // {"schema": "aten::_conj_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _neg_view_copy(const at::Tensor & self); // {"schema": "aten::_neg_view_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor as_strided_copy(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset); // {"schema": "aten::as_strided_copy(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _sparse_broadcast_to_copy(const at::Tensor & self, at::IntArrayRef size); // {"schema": "aten::_sparse_broadcast_to_copy(Tensor self, int[] size) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor diagonal_copy(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2); // {"schema": "aten::diagonal_copy(Tensor self, int offset=0, int dim1=0, int dim2=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor expand_copy(const at::Tensor & self, c10::SymIntArrayRef size, bool implicit); // {"schema": "aten::expand_copy(Tensor self, SymInt[] size, *, bool implicit=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor permute_copy(const at::Tensor & self, at::IntArrayRef dims); // {"schema": "aten::permute_copy(Tensor self, int[] dims) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _reshape_alias_copy(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride); // {"schema": "aten::_reshape_alias_copy(Tensor self, SymInt[] size, SymInt[] stride) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor select_copy(const at::Tensor & self, int64_t dim, c10::SymInt index); // {"schema": "aten::select_copy.int(Tensor self, int dim, SymInt index) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor detach_copy(const at::Tensor & self); // {"schema": "aten::detach_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor slice_copy(const at::Tensor & self, int64_t dim, ::std::optional<c10::SymInt> start, ::std::optional<c10::SymInt> end, c10::SymInt step); // {"schema": "aten::slice_copy.Tensor(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> split_copy(const at::Tensor & self, c10::SymInt split_size, int64_t dim); // {"schema": "aten::split_copy.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[]", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> split_with_sizes_copy(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim); // {"schema": "aten::split_with_sizes_copy(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[]", "dispatch": "True", "default": "True"}
at::Tensor squeeze_copy(const at::Tensor & self); // {"schema": "aten::squeeze_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor squeeze_copy(const at::Tensor & self, int64_t dim); // {"schema": "aten::squeeze_copy.dim(Tensor self, int dim) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor squeeze_copy(const at::Tensor & self, at::IntArrayRef dim); // {"schema": "aten::squeeze_copy.dims(Tensor self, int[] dim) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor t_copy(const at::Tensor & self); // {"schema": "aten::t_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor transpose_copy(const at::Tensor & self, int64_t dim0, int64_t dim1); // {"schema": "aten::transpose_copy.int(Tensor self, int dim0, int dim1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor unsqueeze_copy(const at::Tensor & self, int64_t dim); // {"schema": "aten::unsqueeze_copy(Tensor self, int dim) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _indices_copy(const at::Tensor & self); // {"schema": "aten::_indices_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _values_copy(const at::Tensor & self); // {"schema": "aten::_values_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor indices_copy(const at::Tensor & self); // {"schema": "aten::indices_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor values_copy(const at::Tensor & self); // {"schema": "aten::values_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor crow_indices_copy(const at::Tensor & self); // {"schema": "aten::crow_indices_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor col_indices_copy(const at::Tensor & self); // {"schema": "aten::col_indices_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor ccol_indices_copy(const at::Tensor & self); // {"schema": "aten::ccol_indices_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor row_indices_copy(const at::Tensor & self); // {"schema": "aten::row_indices_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> unbind_copy(const at::Tensor & self, int64_t dim); // {"schema": "aten::unbind_copy.int(Tensor self, int dim=0) -> Tensor[]", "dispatch": "True", "default": "True"}
void unbind_copy_out(const at::Tensor & self, int64_t dim, at::TensorList out); // {"schema": "aten::unbind_copy.int_out(Tensor self, int dim=0, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void split_copy_out(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out); // {"schema": "aten::split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void split_with_sizes_copy_out(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out); // {"schema": "aten::split_with_sizes_copy.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
at::Tensor view_copy(const at::Tensor & self, c10::SymIntArrayRef size); // {"schema": "aten::view_copy(Tensor self, SymInt[] size) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor view_copy(const at::Tensor & self, at::ScalarType dtype); // {"schema": "aten::view_copy.dtype(Tensor self, ScalarType dtype) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor unfold_copy(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step); // {"schema": "aten::unfold_copy(Tensor self, int dimension, int size, int step) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor alias_copy(const at::Tensor & self); // {"schema": "aten::alias_copy(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor to_padded_tensor(const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size); // {"schema": "aten::to_padded_tensor(Tensor self, float padding, SymInt[]? output_size=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _jagged_to_padded_dense_forward(const at::Tensor & values, at::TensorList offsets, c10::SymIntArrayRef max_lengths, double padding_value); // {"schema": "aten::_jagged_to_padded_dense_forward(Tensor values, Tensor[] offsets, SymInt[] max_lengths, float padding_value=0.0) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _padded_dense_to_jagged_forward(const at::Tensor & dense, at::TensorList offsets, ::std::optional<c10::SymInt> total_L); // {"schema": "aten::_padded_dense_to_jagged_forward(Tensor dense, Tensor[] offsets, SymInt? total_L=None) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _nested_from_padded_tensor(const at::Tensor & padded, const at::Tensor & offsets, const at::Tensor & dummy, int64_t ragged_idx, const ::std::optional<at::Tensor> & min_seqlen, const ::std::optional<at::Tensor> & max_seqlen, ::std::optional<c10::SymInt> sum_S); // {"schema": "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", "dispatch": "True", "default": "False"}
at::Tensor _nested_tensor_softmax_with_shape(const at::Tensor & self, const at::Tensor & query); // {"schema": "aten::_nested_tensor_softmax_with_shape(Tensor self, Tensor query) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor _safe_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype); // {"schema": "aten::_safe_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor _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<at::Tensor> & mask, ::std::optional<int64_t> mask_type); // {"schema": "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", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _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<at::Tensor> & mask, bool need_weights, bool average_attn_weights, ::std::optional<int64_t> mask_type); // {"schema": "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)", "dispatch": "True", "default": "False"}
at::Tensor scaled_dot_product_attention(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & attn_mask, double dropout_p, bool is_causal, ::std::optional<double> scale, bool enable_gqa); // {"schema": "aten::scaled_dot_product_attention(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) -> Tensor", "dispatch": "False", "default": "True"}
int64_t _fused_sdp_choice(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & attn_mask, double dropout_p, bool is_causal, ::std::optional<double> scale, bool enable_gqa); // {"schema": "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", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _scaled_dot_product_attention_math(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & attn_mask, double dropout_p, bool is_causal, const ::std::optional<at::Tensor> & dropout_mask, ::std::optional<double> scale, bool enable_gqa); // {"schema": "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)", "dispatch": "False", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> _scaled_dot_product_attention_math_for_mps(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & attn_mask, double dropout_p, bool is_causal, const ::std::optional<at::Tensor> & dropout_mask, ::std::optional<double> scale, bool enable_gqa); // {"schema": "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, bool enable_gqa=False) -> (Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt,at::Tensor,at::Tensor,at::Tensor> _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<double> scale); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt,at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_flash_attention(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & q_descale, const ::std::optional<at::Tensor> & k_descale, const ::std::optional<at::Tensor> & v_descale, double dropout_p, bool is_causal, bool return_debug_mask, ::std::optional<double> scale); // {"schema": "aten::_scaled_dot_product_flash_attention.quantized(Tensor query, Tensor key, Tensor value, Tensor? q_descale, Tensor? k_descale, Tensor? v_descale, 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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor> _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<at::Tensor> & attn_mask, ::std::optional<double> scale); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt,at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_fused_attention_overrideable(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & attn_bias, double dropout_p, bool is_causal, bool return_debug_mask, ::std::optional<double> scale); // {"schema": "aten::_scaled_dot_product_fused_attention_overrideable(Tensor query, Tensor key, Tensor value, Tensor? attn_bias=None, 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)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_flash_attention_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, ::std::optional<double> scale); // {"schema": "aten::_scaled_dot_product_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor philox_seed, Tensor philox_offset, *, float? scale=None) -> (Tensor grad_query, Tensor grad_key, Tensor grad_value)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_flash_attention_for_cpu_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, double dropout_p, bool is_causal, const ::std::optional<at::Tensor> & attn_mask, ::std::optional<double> scale); // {"schema": "aten::_scaled_dot_product_flash_attention_for_cpu_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, float dropout_p, bool is_causal, *, Tensor? attn_mask=None, float? scale=None) -> (Tensor grad_query, Tensor grad_key, Tensor grad_value)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_fused_attention_overrideable_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & attn_bias, ::std::array<bool,4> grad_input_mask, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, ::std::optional<double> scale); // {"schema": "aten::_scaled_dot_product_fused_attention_overrideable_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor attn_bias, bool[4] grad_input_mask, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor philox_seed, Tensor philox_offset, *, float? scale=None) -> (Tensor grad_query, Tensor grad_key, Tensor grad_value, Tensor grad_attn_bias)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_efficient_attention(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & attn_bias, bool compute_log_sumexp, double dropout_p, bool is_causal, ::std::optional<double> scale); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_efficient_attention_backward(const at::Tensor & grad_out_, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & attn_bias, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & philox_seed, const at::Tensor & philox_offset, double dropout_p, ::std::array<bool,4> grad_input_mask, bool is_causal, ::std::optional<double> scale); // {"schema": "aten::_scaled_dot_product_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor attn_bias, Tensor out, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset, float dropout_p, bool[4] grad_input_mask, bool is_causal=False, *, float? scale=None) -> (Tensor, Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt,at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_cudnn_attention(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & attn_bias, bool compute_log_sumexp, double dropout_p, bool is_causal, bool return_debug_mask, ::std::optional<double> scale); // {"schema": "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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_cudnn_attention_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & philox_seed, const at::Tensor & philox_offset, const at::Tensor & attn_bias, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, ::std::optional<double> scale); // {"schema": "aten::_scaled_dot_product_cudnn_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset, Tensor attn_bias, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, *, float? scale=None) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _flash_attention_forward(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & cum_seq_q, const ::std::optional<at::Tensor> & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, bool return_debug_mask, ::std::optional<double> scale, ::std::optional<c10::SymInt> window_size_left, ::std::optional<c10::SymInt> window_size_right, const ::std::optional<at::Tensor> & seqused_k, const ::std::optional<at::Tensor> & alibi_slopes, const ::std::optional<at::Tensor> & block_table, ::std::optional<int64_t> num_splits); // {"schema": "aten::_flash_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? cum_seq_q, Tensor? cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, bool return_debug_mask, *, float? scale=None, SymInt? window_size_left=None, SymInt? window_size_right=None, Tensor? seqused_k=None, Tensor? alibi_slopes=None, Tensor? block_table=None, int? num_splits=None) -> (Tensor output, Tensor softmax_logsumexp, Tensor rng_state, Tensor unused, Tensor debug_attn_mask)", "dispatch": "True", "default": "False"}
at::Tensor _flash_attention_forward_no_dropout_inplace(at::Tensor & out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & cum_seq_q, const ::std::optional<at::Tensor> & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, bool return_debug_mask, ::std::optional<double> scale, ::std::optional<c10::SymInt> window_size_left, ::std::optional<c10::SymInt> window_size_right, const ::std::optional<at::Tensor> & seqused_k, const ::std::optional<at::Tensor> & alibi_slopes, const ::std::optional<at::Tensor> & block_table, ::std::optional<int64_t> num_splits); // {"schema": "aten::_flash_attention_forward_no_dropout_inplace(Tensor(a!) out, Tensor query, Tensor key, Tensor value, Tensor? cum_seq_q, Tensor? cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, bool return_debug_mask, *, float? scale=None, SymInt? window_size_left=None, SymInt? window_size_right=None, Tensor? seqused_k=None, Tensor? alibi_slopes=None, Tensor? block_table=None, int? num_splits=None) -> Tensor softmax_logsumexp", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _flash_attention_forward(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & cum_seq_q, const ::std::optional<at::Tensor> & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, bool return_debug_mask, const ::std::optional<at::Tensor> & q_descale, const ::std::optional<at::Tensor> & k_descale, const ::std::optional<at::Tensor> & v_descale, ::std::optional<double> scale, ::std::optional<c10::SymInt> window_size_left, ::std::optional<c10::SymInt> window_size_right, const ::std::optional<at::Tensor> & seqused_k, const ::std::optional<at::Tensor> & alibi_slopes); // {"schema": "aten::_flash_attention_forward.quantized(Tensor query, Tensor key, Tensor value, Tensor? cum_seq_q, Tensor? cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, bool return_debug_mask, Tensor? q_descale, Tensor? k_descale, Tensor? v_descale, *, float? scale=None, SymInt? window_size_left=None, SymInt? window_size_right=None, Tensor? seqused_k=None, Tensor? alibi_slopes=None) -> (Tensor output, Tensor softmax_logsumexp, Tensor rng_state, Tensor unused, Tensor debug_attn_mask)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _flash_attention_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, const at::Tensor & rng_state, const at::Tensor & unused, ::std::optional<double> scale, ::std::optional<c10::SymInt> window_size_left, ::std::optional<c10::SymInt> window_size_right); // {"schema": "aten::_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor rng_state, Tensor unused, *, float? scale=None, SymInt? window_size_left=None, SymInt? window_size_right=None) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt> _efficient_attention_forward(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & cu_seqlens_q, const ::std::optional<at::Tensor> & cu_seqlens_k, ::std::optional<c10::SymInt> max_seqlen_q, ::std::optional<c10::SymInt> max_seqlen_k, double dropout_p, int64_t custom_mask_type, bool compute_log_sumexp, ::std::optional<double> scale, const ::std::optional<at::Tensor> & seqlen_k, ::std::optional<int64_t> window_size); // {"schema": "aten::_efficient_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? bias, Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, SymInt? max_seqlen_q, SymInt? max_seqlen_k, float dropout_p, int custom_mask_type, bool compute_log_sumexp=False, *, float? scale=None, Tensor? seqlen_k=None, int? window_size=None) -> (Tensor output, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset, SymInt max_seqlen_batch_q, SymInt max_seqlen_batch_k)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _efficient_attention_backward(const at::Tensor & grad_out_, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & bias, const at::Tensor & out, const ::std::optional<at::Tensor> & cu_seqlens_q, const ::std::optional<at::Tensor> & cu_seqlens_k, c10::SymInt max_seqlen_q, c10::SymInt max_seqlen_k, const at::Tensor & logsumexp, double dropout_p, const at::Tensor & philox_seed, const at::Tensor & philox_offset, int64_t custom_mask_type, bool bias_requires_grad, ::std::optional<double> scale, ::std::optional<int64_t> num_splits_key, ::std::optional<int64_t> window_size, bool shared_storage_dqdkdv); // {"schema": "aten::_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor? bias, Tensor out, Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, SymInt max_seqlen_q, SymInt max_seqlen_k, Tensor logsumexp, float dropout_p, Tensor philox_seed, Tensor philox_offset, int custom_mask_type, bool bias_requires_grad, *, float? scale=None, int? num_splits_key=None, int? window_size=None, bool shared_storage_dqdkdv=False) -> (Tensor, Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt,at::Tensor,at::Tensor,at::Tensor> _cudnn_attention_forward(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & attn_bias, const ::std::optional<at::Tensor> & cum_seq_q, const ::std::optional<at::Tensor> & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, bool compute_log_sumexp, double dropout_p, bool is_causal, bool return_debug_mask, ::std::optional<double> scale); // {"schema": "aten::_cudnn_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? attn_bias, Tensor? cum_seq_q, Tensor? cum_seq_k, SymInt max_q, SymInt max_k, 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)", "dispatch": "True", "default": "False"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor> _cudnn_attention_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & philox_seed, const at::Tensor & philox_offset, const at::Tensor & attn_bias, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, ::std::optional<double> scale); // {"schema": "aten::_cudnn_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset, Tensor attn_bias, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, *, float? scale=None) -> (Tensor, Tensor, Tensor)", "dispatch": "True", "default": "False"}
at::Tensor _triton_scaled_dot_attention(const at::Tensor & q, const at::Tensor & k, const at::Tensor & v, double dropout_p); // {"schema": "aten::_triton_scaled_dot_attention(Tensor q, Tensor k, Tensor v, float dropout_p=0.0) -> Tensor", "dispatch": "True", "default": "False"}
at::Tensor & _fill_mem_eff_dropout_mask_(at::Tensor & self, double dropout_p, int64_t seed, int64_t offset); // {"schema": "aten::_fill_mem_eff_dropout_mask_(Tensor(a!) self, float dropout_p, int seed, int offset) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _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<at::Tensor> & mask); // {"schema": "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", "dispatch": "True", "default": "False"}
at::Tensor special_airy_ai(const at::Tensor & x); // {"schema": "aten::special_airy_ai(Tensor x) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_airy_ai_out(const at::Tensor & x, at::Tensor & out); // {"schema": "aten::special_airy_ai.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_bessel_j0(const at::Tensor & self); // {"schema": "aten::special_bessel_j0(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_bessel_j0_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_bessel_j0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_bessel_j1(const at::Tensor & self); // {"schema": "aten::special_bessel_j1(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_bessel_j1_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_bessel_j1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_bessel_y0(const at::Tensor & self); // {"schema": "aten::special_bessel_y0(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_bessel_y0_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_bessel_y0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_bessel_y1(const at::Tensor & self); // {"schema": "aten::special_bessel_y1(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_bessel_y1_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_bessel_y1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_chebyshev_polynomial_t(const at::Tensor & x, const at::Tensor & n); // {"schema": "aten::special_chebyshev_polynomial_t(Tensor x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_chebyshev_polynomial_t(const at::Scalar & x, const at::Tensor & n); // {"schema": "aten::special_chebyshev_polynomial_t.x_scalar(Scalar x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_chebyshev_polynomial_t(const at::Tensor & x, const at::Scalar & n); // {"schema": "aten::special_chebyshev_polynomial_t.n_scalar(Tensor x, Scalar n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_chebyshev_polynomial_t_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & special_chebyshev_polynomial_t_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & special_chebyshev_polynomial_t_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out); // {"schema": "aten::special_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor special_chebyshev_polynomial_u(const at::Tensor & x, const at::Tensor & n); // {"schema": "aten::special_chebyshev_polynomial_u(Tensor x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_chebyshev_polynomial_u(const at::Scalar & x, const at::Tensor & n); // {"schema": "aten::special_chebyshev_polynomial_u.x_scalar(Scalar x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_chebyshev_polynomial_u(const at::Tensor & x, const at::Scalar & n); // {"schema": "aten::special_chebyshev_polynomial_u.n_scalar(Tensor x, Scalar n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_chebyshev_polynomial_u_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_chebyshev_polynomial_u.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & special_chebyshev_polynomial_u_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_chebyshev_polynomial_u.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & special_chebyshev_polynomial_u_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out); // {"schema": "aten::special_chebyshev_polynomial_u.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor special_chebyshev_polynomial_v(const at::Tensor & x, const at::Tensor & n); // {"schema": "aten::special_chebyshev_polynomial_v(Tensor x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_chebyshev_polynomial_v(const at::Scalar & x, const at::Tensor & n); // {"schema": "aten::special_chebyshev_polynomial_v.x_scalar(Scalar x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_chebyshev_polynomial_v(const at::Tensor & x, const at::Scalar & n); // {"schema": "aten::special_chebyshev_polynomial_v.n_scalar(Tensor x, Scalar n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_chebyshev_polynomial_v_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & special_chebyshev_polynomial_v_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & special_chebyshev_polynomial_v_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out); // {"schema": "aten::special_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor special_chebyshev_polynomial_w(const at::Tensor & x, const at::Tensor & n); // {"schema": "aten::special_chebyshev_polynomial_w(Tensor x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_chebyshev_polynomial_w(const at::Scalar & x, const at::Tensor & n); // {"schema": "aten::special_chebyshev_polynomial_w.x_scalar(Scalar x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_chebyshev_polynomial_w(const at::Tensor & x, const at::Scalar & n); // {"schema": "aten::special_chebyshev_polynomial_w.n_scalar(Tensor x, Scalar n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_chebyshev_polynomial_w_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & special_chebyshev_polynomial_w_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & special_chebyshev_polynomial_w_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out); // {"schema": "aten::special_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor special_hermite_polynomial_h(const at::Tensor & x, const at::Tensor & n); // {"schema": "aten::special_hermite_polynomial_h(Tensor x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_hermite_polynomial_h(const at::Scalar & x, const at::Tensor & n); // {"schema": "aten::special_hermite_polynomial_h.x_scalar(Scalar x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_hermite_polynomial_h(const at::Tensor & x, const at::Scalar & n); // {"schema": "aten::special_hermite_polynomial_h.n_scalar(Tensor x, Scalar n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_hermite_polynomial_h_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_hermite_polynomial_h.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & special_hermite_polynomial_h_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_hermite_polynomial_h.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & special_hermite_polynomial_h_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out); // {"schema": "aten::special_hermite_polynomial_h.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor special_hermite_polynomial_he(const at::Tensor & x, const at::Tensor & n); // {"schema": "aten::special_hermite_polynomial_he(Tensor x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_hermite_polynomial_he(const at::Scalar & x, const at::Tensor & n); // {"schema": "aten::special_hermite_polynomial_he.x_scalar(Scalar x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_hermite_polynomial_he(const at::Tensor & x, const at::Scalar & n); // {"schema": "aten::special_hermite_polynomial_he.n_scalar(Tensor x, Scalar n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_hermite_polynomial_he_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_hermite_polynomial_he.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & special_hermite_polynomial_he_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_hermite_polynomial_he.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & special_hermite_polynomial_he_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out); // {"schema": "aten::special_hermite_polynomial_he.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor special_laguerre_polynomial_l(const at::Tensor & x, const at::Tensor & n); // {"schema": "aten::special_laguerre_polynomial_l(Tensor x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_laguerre_polynomial_l(const at::Scalar & x, const at::Tensor & n); // {"schema": "aten::special_laguerre_polynomial_l.x_scalar(Scalar x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_laguerre_polynomial_l(const at::Tensor & x, const at::Scalar & n); // {"schema": "aten::special_laguerre_polynomial_l.n_scalar(Tensor x, Scalar n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_laguerre_polynomial_l_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_laguerre_polynomial_l.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & special_laguerre_polynomial_l_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_laguerre_polynomial_l.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & special_laguerre_polynomial_l_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out); // {"schema": "aten::special_laguerre_polynomial_l.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor special_legendre_polynomial_p(const at::Tensor & x, const at::Tensor & n); // {"schema": "aten::special_legendre_polynomial_p(Tensor x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_legendre_polynomial_p(const at::Scalar & x, const at::Tensor & n); // {"schema": "aten::special_legendre_polynomial_p.x_scalar(Scalar x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_legendre_polynomial_p(const at::Tensor & x, const at::Scalar & n); // {"schema": "aten::special_legendre_polynomial_p.n_scalar(Tensor x, Scalar n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_legendre_polynomial_p_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_legendre_polynomial_p.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & special_legendre_polynomial_p_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_legendre_polynomial_p.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & special_legendre_polynomial_p_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out); // {"schema": "aten::special_legendre_polynomial_p.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor special_modified_bessel_i0(const at::Tensor & self); // {"schema": "aten::special_modified_bessel_i0(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_modified_bessel_i0_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_modified_bessel_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_modified_bessel_i1(const at::Tensor & self); // {"schema": "aten::special_modified_bessel_i1(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_modified_bessel_i1_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_modified_bessel_i1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_modified_bessel_k0(const at::Tensor & self); // {"schema": "aten::special_modified_bessel_k0(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_modified_bessel_k0_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_modified_bessel_k0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_modified_bessel_k1(const at::Tensor & self); // {"schema": "aten::special_modified_bessel_k1(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_modified_bessel_k1_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::special_modified_bessel_k1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_scaled_modified_bessel_k0(const at::Tensor & x); // {"schema": "aten::special_scaled_modified_bessel_k0(Tensor x) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_scaled_modified_bessel_k0_out(const at::Tensor & x, at::Tensor & out); // {"schema": "aten::special_scaled_modified_bessel_k0.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_scaled_modified_bessel_k1(const at::Tensor & x); // {"schema": "aten::special_scaled_modified_bessel_k1(Tensor x) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_scaled_modified_bessel_k1_out(const at::Tensor & x, at::Tensor & out); // {"schema": "aten::special_scaled_modified_bessel_k1.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor special_shifted_chebyshev_polynomial_t(const at::Tensor & x, const at::Tensor & n); // {"schema": "aten::special_shifted_chebyshev_polynomial_t(Tensor x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_shifted_chebyshev_polynomial_t(const at::Scalar & x, const at::Tensor & n); // {"schema": "aten::special_shifted_chebyshev_polynomial_t.x_scalar(Scalar x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_shifted_chebyshev_polynomial_t(const at::Tensor & x, const at::Scalar & n); // {"schema": "aten::special_shifted_chebyshev_polynomial_t.n_scalar(Tensor x, Scalar n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_shifted_chebyshev_polynomial_t_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_shifted_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & special_shifted_chebyshev_polynomial_t_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_shifted_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & special_shifted_chebyshev_polynomial_t_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out); // {"schema": "aten::special_shifted_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor special_shifted_chebyshev_polynomial_u(const at::Tensor & x, const at::Tensor & n); // {"schema": "aten::special_shifted_chebyshev_polynomial_u(Tensor x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_shifted_chebyshev_polynomial_u(const at::Scalar & x, const at::Tensor & n); // {"schema": "aten::special_shifted_chebyshev_polynomial_u.x_scalar(Scalar x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_shifted_chebyshev_polynomial_u(const at::Tensor & x, const at::Scalar & n); // {"schema": "aten::special_shifted_chebyshev_polynomial_u.n_scalar(Tensor x, Scalar n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_shifted_chebyshev_polynomial_u_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_shifted_chebyshev_polynomial_u.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & special_shifted_chebyshev_polynomial_u_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_shifted_chebyshev_polynomial_u.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & special_shifted_chebyshev_polynomial_u_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out); // {"schema": "aten::special_shifted_chebyshev_polynomial_u.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor special_shifted_chebyshev_polynomial_v(const at::Tensor & x, const at::Tensor & n); // {"schema": "aten::special_shifted_chebyshev_polynomial_v(Tensor x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_shifted_chebyshev_polynomial_v(const at::Scalar & x, const at::Tensor & n); // {"schema": "aten::special_shifted_chebyshev_polynomial_v.x_scalar(Scalar x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_shifted_chebyshev_polynomial_v(const at::Tensor & x, const at::Scalar & n); // {"schema": "aten::special_shifted_chebyshev_polynomial_v.n_scalar(Tensor x, Scalar n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_shifted_chebyshev_polynomial_v_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_shifted_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & special_shifted_chebyshev_polynomial_v_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_shifted_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & special_shifted_chebyshev_polynomial_v_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out); // {"schema": "aten::special_shifted_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor special_shifted_chebyshev_polynomial_w(const at::Tensor & x, const at::Tensor & n); // {"schema": "aten::special_shifted_chebyshev_polynomial_w(Tensor x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_shifted_chebyshev_polynomial_w(const at::Scalar & x, const at::Tensor & n); // {"schema": "aten::special_shifted_chebyshev_polynomial_w.x_scalar(Scalar x, Tensor n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor special_shifted_chebyshev_polynomial_w(const at::Tensor & x, const at::Scalar & n); // {"schema": "aten::special_shifted_chebyshev_polynomial_w.n_scalar(Tensor x, Scalar n) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_shifted_chebyshev_polynomial_w_out(const at::Tensor & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_shifted_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor & special_shifted_chebyshev_polynomial_w_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out); // {"schema": "aten::special_shifted_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & special_shifted_chebyshev_polynomial_w_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out); // {"schema": "aten::special_shifted_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor special_spherical_bessel_j0(const at::Tensor & x); // {"schema": "aten::special_spherical_bessel_j0(Tensor x) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & special_spherical_bessel_j0_out(const at::Tensor & x, at::Tensor & out); // {"schema": "aten::special_spherical_bessel_j0.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "False"}
at::Tensor _foobar(const at::Tensor & self, bool arg1, bool arg2, bool arg3); // {"schema": "aten::_foobar(Tensor self, bool arg1=True, bool arg2=True, *, bool arg3=True) -> Tensor", "dispatch": "True", "default": "False"}
void _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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf); // {"schema": "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) -> ()", "dispatch": "True", "default": "False"}
void _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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf); // {"schema": "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) -> ()", "dispatch": "True", "default": "False"}
void _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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf); // {"schema": "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) -> ()", "dispatch": "True", "default": "False"}
void _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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf); // {"schema": "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) -> ()", "dispatch": "True", "default": "False"}
void _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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf); // {"schema": "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) -> ()", "dispatch": "True", "default": "False"}
void _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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf); // {"schema": "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) -> ()", "dispatch": "True", "default": "False"}
void _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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf); // {"schema": "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) -> ()", "dispatch": "True", "default": "False"}
void _fused_adagrad_(at::TensorList self, at::TensorList grads, at::TensorList state_sums, at::TensorList state_steps, const at::Tensor & lr, double lr_decay, double weight_decay, double eps, bool maximize, const ::std::optional<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf); // {"schema": "aten::_fused_adagrad_.tensor_lr(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] state_sums, Tensor[] state_steps, *, Tensor lr, float lr_decay, float weight_decay, float eps, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> ()", "dispatch": "True", "default": "False"}
void _propagate_xla_data(const at::Tensor & input, const at::Tensor & output); // {"schema": "aten::_propagate_xla_data(Tensor input, Tensor output) -> ()", "dispatch": "False", "default": "True"}
at::Tensor & _new_zeros_with_same_feature_meta_out(const at::Tensor & self, const at::Tensor & other, int64_t self_num_batch_dims, at::Tensor & out); // {"schema": "aten::_new_zeros_with_same_feature_meta.out(Tensor self, Tensor other, *, int self_num_batch_dims=0, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> _cudnn_ctc_loss_out(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, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::_cudnn_ctc_loss.out(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank, bool deterministic, bool zero_infinity, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
at::Tensor & _cudnn_rnn_flatten_weight_out(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 & out); // {"schema": "aten::_cudnn_rnn_flatten_weight.out(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(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_out(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const ::std::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & 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<at::Tensor> & dropout_state, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4); // {"schema": "aten::_cudnn_rnn.out(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(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))", "dispatch": "True", "default": "True"}
void _cudnn_rnn_backward_out(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, 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<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3); // {"schema": "aten::_cudnn_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, 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 reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> ()", "dispatch": "True", "default": "True"}
at::Tensor & _cudnn_init_dropout_state_out(double dropout, bool train, int64_t dropout_seed, at::Tensor & out); // {"schema": "aten::_cudnn_init_dropout_state.out(float dropout, bool train, int dropout_seed, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> _fused_dropout_out(const at::Tensor & self, double p, ::std::optional<at::Generator> generator, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::_fused_dropout.out(Tensor self, float p, Generator? generator=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
at::Tensor & _masked_scale_out(const at::Tensor & self, const at::Tensor & mask, double scale, at::Tensor & out); // {"schema": "aten::_masked_scale.out(Tensor self, Tensor mask, float scale, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> native_dropout_out(const at::Tensor & input, double p, ::std::optional<bool> train, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::native_dropout.out(Tensor input, float p, bool? train, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
at::Tensor & native_dropout_backward_out(const at::Tensor & grad_output, const at::Tensor & mask, double scale, at::Tensor & out); // {"schema": "aten::native_dropout_backward.out(Tensor grad_output, Tensor mask, float scale, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _conj_physical_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::_conj_physical.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & avg_pool1d_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, at::Tensor & out); // {"schema": "aten::avg_pool1d.out(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, bool ceil_mode=False, bool count_include_pad=True, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & adaptive_avg_pool1d_out(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out); // {"schema": "aten::adaptive_avg_pool1d.out(Tensor self, int[1] output_size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _add_relu_out(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::_add_relu.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & add_out(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::add.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & affine_grid_generator_out(const at::Tensor & theta, c10::SymIntArrayRef size, bool align_corners, at::Tensor & out); // {"schema": "aten::affine_grid_generator.out(Tensor theta, SymInt[] size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _test_functorch_fallback_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::_test_functorch_fallback.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bartlett_window_out(int64_t window_length, at::Tensor & out); // {"schema": "aten::bartlett_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bartlett_window_out(int64_t window_length, bool periodic, at::Tensor & out); // {"schema": "aten::bartlett_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & quantized_batch_norm_out(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & mean, const at::Tensor & var, double eps, double output_scale, int64_t output_zero_point, at::Tensor & out); // {"schema": "aten::quantized_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bernoulli_out(const at::Tensor & self, const at::Tensor & p, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::bernoulli.Tensor_out(Tensor self, Tensor p, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor bernoulli(const at::Tensor & self, const at::Tensor & p, ::std::optional<at::Generator> generator); // {"schema": "aten::bernoulli.Tensor(Tensor self, Tensor p, *, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & bernoulli_out(const at::Tensor & self, double p, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::bernoulli.float_out(Tensor self, float p=0.5, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & binary_cross_entropy_with_logits_out(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & pos_weight, int64_t reduction, at::Tensor & out); // {"schema": "aten::binary_cross_entropy_with_logits.out(Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bincount_out(const at::Tensor & self, const ::std::optional<at::Tensor> & weights, c10::SymInt minlength, at::Tensor & out); // {"schema": "aten::bincount.out(Tensor self, Tensor? weights=None, SymInt minlength=0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & blackman_window_out(int64_t window_length, at::Tensor & out); // {"schema": "aten::blackman_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & blackman_window_out(int64_t window_length, bool periodic, at::Tensor & out); // {"schema": "aten::blackman_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & block_diag_out(at::TensorList tensors, at::Tensor & out); // {"schema": "aten::block_diag.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & constant_pad_nd_out(const at::Tensor & self, c10::SymIntArrayRef pad, const at::Scalar & value, at::Tensor & out); // {"schema": "aten::constant_pad_nd.out(Tensor self, SymInt[] pad, Scalar value=0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & convolution_out(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, at::Tensor & out); // {"schema": "aten::convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_out(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalSymIntArrayRef bias_sizes, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::convolution_backward.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
at::Tensor & convolution_overrideable_out(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, at::Tensor & out); // {"schema": "aten::convolution_overrideable.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_overrideable_out(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::convolution_backward_overrideable.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
at::Tensor & _convolution_out(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & 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 & out); // {"schema": "aten::_convolution.out(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(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & conv_tbc_out(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad, at::Tensor & out); // {"schema": "aten::conv_tbc.out(Tensor self, Tensor weight, Tensor bias, int pad=0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & copy_out(const at::Tensor & self, const at::Tensor & src, bool non_blocking, at::Tensor & out); // {"schema": "aten::copy.out(Tensor self, Tensor src, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _copy_from_out(const at::Tensor & self, const at::Tensor & dst, bool non_blocking, at::Tensor & out); // {"schema": "aten::_copy_from.out(Tensor self, Tensor dst, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _copy_from_and_resize_out(const at::Tensor & self, const at::Tensor & dst, at::Tensor & out); // {"schema": "aten::_copy_from_and_resize.out(Tensor self, Tensor dst, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & count_nonzero_out(const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out); // {"schema": "aten::count_nonzero.dim_IntList_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & count_nonzero_out(const at::Tensor & self, ::std::optional<int64_t> dim, at::Tensor & out); // {"schema": "aten::count_nonzero.out(Tensor self, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & cudnn_affine_grid_generator_out(const at::Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W, at::Tensor & out); // {"schema": "aten::cudnn_affine_grid_generator.out(Tensor theta, int N, int C, int H, int W, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & cudnn_affine_grid_generator_backward_out(const at::Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W, at::Tensor & out); // {"schema": "aten::cudnn_affine_grid_generator_backward.out(Tensor grad, int N, int C, int H, int W, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> cudnn_batch_norm_backward_out(const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_var, double epsilon, const at::Tensor & reserveSpace, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::cudnn_batch_norm_backward.out(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon, Tensor reserveSpace, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
at::Tensor & cudnn_convolution_transpose_out(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 & out); // {"schema": "aten::cudnn_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _mps_convolution_transpose_out(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 & out); // {"schema": "aten::_mps_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> mps_convolution_transpose_backward_out(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,2> output_mask, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::mps_convolution_transpose_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
at::Tensor & cudnn_convolution_relu_out(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out); // {"schema": "aten::cudnn_convolution_relu.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & cudnn_convolution_add_relu_out(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out); // {"schema": "aten::cudnn_convolution_add_relu.out(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & cudnn_grid_sampler_out(const at::Tensor & self, const at::Tensor & grid, at::Tensor & out); // {"schema": "aten::cudnn_grid_sampler.out(Tensor self, Tensor grid, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> cudnn_grid_sampler_backward_out(const at::Tensor & self, const at::Tensor & grid, const at::Tensor & grad_output, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::cudnn_grid_sampler_backward.out(Tensor self, Tensor grid, Tensor grad_output, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> _ctc_loss_out(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool zero_infinity, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::_ctc_loss.out(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> _ctc_loss_out(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, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::_ctc_loss.Tensor_out(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
at::Tensor & _ctc_loss_backward_out(const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity, at::Tensor & out); // {"schema": "aten::_ctc_loss_backward.out(Tensor grad, Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, Tensor neg_log_likelihood, Tensor log_alpha, int blank, bool zero_infinity=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & diag_embed_out(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out); // {"schema": "aten::diag_embed.out(Tensor self, int offset=0, int dim1=-2, int dim2=-1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & diagonal_backward_out(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out); // {"schema": "aten::diagonal_backward.out(Tensor grad_output, SymInt[] input_sizes, int offset, int dim1, int dim2, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & div_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::div.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & div_out(const at::Tensor & self, const at::Scalar & other, ::std::optional<c10::string_view> rounding_mode, at::Tensor & out); // {"schema": "aten::div.Scalar_mode_out(Tensor self, Scalar other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & embedding_out(const at::Tensor & weight, const at::Tensor & indices, c10::SymInt padding_idx, bool scale_grad_by_freq, bool sparse, at::Tensor & out); // {"schema": "aten::embedding.out(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & embedding_dense_backward_out(const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq, at::Tensor & out); // {"schema": "aten::embedding_dense_backward.out(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & embedding_renorm_out(const at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type, at::Tensor & out); // {"schema": "aten::embedding_renorm.out(Tensor self, Tensor indices, float max_norm, float norm_type, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor embedding_renorm(const at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type); // {"schema": "aten::embedding_renorm(Tensor self, Tensor indices, float max_norm, float norm_type) -> Tensor", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _embedding_bag_forward_only_out(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<at::Tensor> & per_sample_weights, bool include_last_offset, int64_t padding_idx, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3); // {"schema": "aten::_embedding_bag_forward_only.out(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(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _embedding_bag_out(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<at::Tensor> & per_sample_weights, bool include_last_offset, int64_t padding_idx, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3); // {"schema": "aten::_embedding_bag.out(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(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))", "dispatch": "True", "default": "True"}
at::Tensor & _embedding_bag_dense_backward_out(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx, at::Tensor & out); // {"schema": "aten::_embedding_bag_dense_backward.out(Tensor grad, Tensor indices, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _embedding_bag_per_sample_weights_backward_out(const at::Tensor & grad, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, int64_t mode, int64_t padding_idx, at::Tensor & out); // {"schema": "aten::_embedding_bag_per_sample_weights_backward.out(Tensor grad, Tensor weight, Tensor indices, Tensor offsets, Tensor offset2bag, int mode, int padding_idx=-1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & empty_permuted_out(c10::SymIntArrayRef size, at::IntArrayRef physical_layout, at::Tensor & out); // {"schema": "aten::empty_permuted.out(SymInt[] size, int[] physical_layout, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & new_empty_out(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out); // {"schema": "aten::new_empty.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & new_empty_strided_out(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out); // {"schema": "aten::new_empty_strided.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & new_full_out(const at::Tensor & self, c10::SymIntArrayRef size, const at::Scalar & fill_value, at::Tensor & out); // {"schema": "aten::new_full.out(Tensor self, SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & new_zeros_out(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out); // {"schema": "aten::new_zeros.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & new_ones_out(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out); // {"schema": "aten::new_ones.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _empty_affine_quantized_out(c10::SymIntArrayRef size, double scale, int64_t zero_point, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::_empty_affine_quantized.out(SymInt[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _empty_per_channel_affine_quantized_out(c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::_empty_per_channel_affine_quantized.out(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
const at::Tensor & resize_out(const at::Tensor & self, c10::SymIntArrayRef size, ::std::optional<at::MemoryFormat> memory_format, const at::Tensor & out); // {"schema": "aten::resize.out(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor resize(const at::Tensor & self, c10::SymIntArrayRef size, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::resize(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "True", "default": "True"}
const at::Tensor & _resize_output_out(const at::Tensor & self, c10::SymIntArrayRef size, at::Device device, const at::Tensor & out); // {"schema": "aten::_resize_output.out(Tensor self, SymInt[] size, Device device, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor _resize_output(const at::Tensor & self, c10::SymIntArrayRef size, at::Device device); // {"schema": "aten::_resize_output(Tensor self, SymInt[] size, Device device) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & empty_quantized_out(at::IntArrayRef size, const at::Tensor & qtensor, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::empty_quantized.out(int[] size, Tensor qtensor, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & empty_like_out(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::empty_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & empty_strided_out(c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out); // {"schema": "aten::empty_strided.out(SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & fill_out(const at::Tensor & self, const at::Scalar & value, at::Tensor & out); // {"schema": "aten::fill.Scalar_out(Tensor self, Scalar value, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & fill_out(const at::Tensor & self, const at::Tensor & value, at::Tensor & out); // {"schema": "aten::fill.Tensor_out(Tensor self, Tensor value, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & floor_divide_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::floor_divide.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & full_like_out(const at::Tensor & self, const at::Scalar & fill_value, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::full_like.out(Tensor self, Scalar fill_value, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & from_file_out(c10::string_view filename, ::std::optional<bool> shared, ::std::optional<int64_t> size, at::Tensor & out); // {"schema": "aten::from_file.out(str filename, bool? shared=None, int? size=0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & grid_sampler_2d_out(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, at::Tensor & out); // {"schema": "aten::grid_sampler_2d.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> grid_sampler_2d_backward_out(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::grid_sampler_2d_backward.out(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
at::Tensor & _grid_sampler_2d_cpu_fallback_out(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, at::Tensor & out); // {"schema": "aten::_grid_sampler_2d_cpu_fallback.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & grid_sampler_3d_out(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, at::Tensor & out); // {"schema": "aten::grid_sampler_3d.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> grid_sampler_3d_backward_out(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::grid_sampler_3d_backward.out(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
at::Tensor & hann_window_out(int64_t window_length, at::Tensor & out); // {"schema": "aten::hann_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & hann_window_out(int64_t window_length, bool periodic, at::Tensor & out); // {"schema": "aten::hann_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & hamming_window_out(int64_t window_length, at::Tensor & out); // {"schema": "aten::hamming_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & hamming_window_out(int64_t window_length, bool periodic, at::Tensor & out); // {"schema": "aten::hamming_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & hamming_window_out(int64_t window_length, bool periodic, double alpha, at::Tensor & out); // {"schema": "aten::hamming_window.periodic_alpha_out(int window_length, bool periodic, float alpha, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & hamming_window_out(int64_t window_length, bool periodic, double alpha, double beta, at::Tensor & out); // {"schema": "aten::hamming_window.periodic_alpha_beta_out(int window_length, bool periodic, float alpha, float beta, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & kaiser_window_out(int64_t window_length, at::Tensor & out); // {"schema": "aten::kaiser_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & kaiser_window_out(int64_t window_length, bool periodic, at::Tensor & out); // {"schema": "aten::kaiser_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & kaiser_window_out(int64_t window_length, bool periodic, double beta, at::Tensor & out); // {"schema": "aten::kaiser_window.beta_out(int window_length, bool periodic, float beta, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_out(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::native_group_norm.out(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_backward_out(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::native_group_norm_backward.out(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
at::Tensor & index_put_out(const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate, at::Tensor & out); // {"schema": "aten::index_put.out(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _index_put_impl_out(const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate, bool unsafe, at::Tensor & out); // {"schema": "aten::_index_put_impl.out(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False, bool unsafe=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor _index_put_impl(const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate, bool unsafe); // {"schema": "aten::_index_put_impl(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False, bool unsafe=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & isnan_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::isnan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_out(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::native_layer_norm.out(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_backward_out(const at::Tensor & grad_out, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::native_layer_norm_backward.out(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linear_backward_out(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
at::Tensor & mkldnn_linear_out(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::Tensor & out); // {"schema": "aten::mkldnn_linear.out(Tensor self, Tensor weight, Tensor? bias=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & mkldnn_linear_backward_input_out(at::IntArrayRef input_size, const at::Tensor & grad_output, const at::Tensor & weight, at::Tensor & out); // {"schema": "aten::mkldnn_linear_backward_input.out(int[] input_size, Tensor grad_output, Tensor weight, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> mkldnn_linear_backward_weights_out(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, bool bias_defined, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::mkldnn_linear_backward_weights.out(Tensor grad_output, Tensor input, Tensor weight, bool bias_defined, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mkldnn_linear_backward_out(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::mkldnn_linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> matmul_backward_out(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & other, ::std::array<bool,2> mask, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::matmul_backward.out(Tensor grad, Tensor self, Tensor other, bool[2] mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> _aminmax_out(const at::Tensor & self, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::_aminmax.out(Tensor self, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> _aminmax_out(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::_aminmax.dim_out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
at::Tensor & max_pool2d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out); // {"schema": "aten::max_pool2d_backward.out(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & mkldnn_max_pool2d_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out); // {"schema": "aten::mkldnn_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & mkldnn_max_pool2d_backward_out(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out); // {"schema": "aten::mkldnn_max_pool2d_backward.out(Tensor grad_output, Tensor output, Tensor input, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & mkldnn_max_pool3d_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out); // {"schema": "aten::mkldnn_max_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & mkldnn_max_pool3d_backward_out(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out); // {"schema": "aten::mkldnn_max_pool3d_backward.out(Tensor grad_output, Tensor output, Tensor input, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & quantized_max_pool1d_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out); // {"schema": "aten::quantized_max_pool1d.out(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & quantized_max_pool2d_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out); // {"schema": "aten::quantized_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & quantized_max_pool3d_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out); // {"schema": "aten::quantized_max_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & median_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::median.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & nanmedian_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::nanmedian.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _mps_convolution_out(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out); // {"schema": "aten::_mps_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mps_convolution_backward_out(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::mps_convolution_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
at::Tensor & mkldnn_convolution_out(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out); // {"schema": "aten::mkldnn_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> mkldnn_rnn_layer_out(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, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3); // {"schema": "aten::mkldnn_rnn_layer.out(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(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> mkldnn_rnn_layer_backward_out(const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, at::Tensor & out5, at::Tensor & out6); // {"schema": "aten::mkldnn_rnn_layer_backward.out(Tensor input, Tensor weight1, Tensor weight2, Tensor weight3, Tensor weight4, Tensor hx_, Tensor cx_tmp, Tensor output, Tensor hy_, Tensor cy_, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, bool reverse, int mode, int hidden_size, int num_layers, bool has_biases, bool train, bool bidirectional, int[] batch_sizes, bool batch_first, Tensor workspace, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4, Tensor(f!) out5, Tensor(g!) out6) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!), Tensor(f!), Tensor(g!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> miopen_batch_norm_out(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double exponential_average_factor, double epsilon, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::miopen_batch_norm.out(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> miopen_batch_norm_backward_out(const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_var, double epsilon, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::miopen_batch_norm_backward.out(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
at::Tensor & miopen_convolution_out(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, at::Tensor & out); // {"schema": "aten::miopen_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & miopen_convolution_transpose_out(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, at::Tensor & out); // {"schema": "aten::miopen_convolution_transpose.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & miopen_depthwise_convolution_out(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, at::Tensor & out); // {"schema": "aten::miopen_depthwise_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> miopen_rnn_out(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & hx, const ::std::optional<at::Tensor> & 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<at::Tensor> & dropout_state, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4); // {"schema": "aten::miopen_rnn.out(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(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))", "dispatch": "True", "default": "True"}
void miopen_rnn_backward_out(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, 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<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3); // {"schema": "aten::miopen_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> ()", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> miopen_ctc_loss_out(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, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::miopen_ctc_loss.out(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank, bool deterministic, bool zero_infinity, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
at::Tensor & _sparse_sparse_matmul_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::_sparse_sparse_matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & mul_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::mul.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _native_batch_norm_legit_functional(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & running_mean, const at::Tensor & running_var, bool training, double momentum, double eps); // {"schema": "aten::_native_batch_norm_legit_functional(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor running_mean_out, Tensor running_var_out)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _native_batch_norm_legit_no_training_out(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::_native_batch_norm_legit_no_training.out(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> batch_norm_stats_out(const at::Tensor & input, double eps, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::batch_norm_stats.out(Tensor input, float eps, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> batch_norm_gather_stats_out(const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum, double eps, int64_t count, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::batch_norm_gather_stats.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> batch_norm_gather_stats_with_counts_out(const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum, double eps, const at::Tensor & counts, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::batch_norm_gather_stats_with_counts.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_batch_norm_backward_out(const at::Tensor & grad_out, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_invstd, bool train, double eps, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::native_batch_norm_backward.out(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> batch_norm_backward_reduce_out(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & weight, bool input_g, bool weight_g, bool bias_g, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3); // {"schema": "aten::batch_norm_backward_reduce.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))", "dispatch": "True", "default": "True"}
at::Tensor & batch_norm_backward_elemt_out(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & weight, const at::Tensor & sum_dy, const at::Tensor & sum_dy_xmu, const at::Tensor & count, at::Tensor & out); // {"schema": "aten::batch_norm_backward_elemt.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor sum_dy, Tensor sum_dy_xmu, Tensor count, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> batch_norm_update_stats_out(const at::Tensor & input, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::batch_norm_update_stats.out(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
at::Tensor & _nnpack_spatial_convolution_out(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, at::Tensor & out); // {"schema": "aten::_nnpack_spatial_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, SymInt[2] stride=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & ones_like_out(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::ones_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _euclidean_dist_out(const at::Tensor & x1, const at::Tensor & x2, at::Tensor & out); // {"schema": "aten::_euclidean_dist.out(Tensor x1, Tensor x2, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _cdist_forward_out(const at::Tensor & x1, const at::Tensor & x2, double p, ::std::optional<int64_t> compute_mode, at::Tensor & out); // {"schema": "aten::_cdist_forward.out(Tensor x1, Tensor x2, float p, int? compute_mode, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _cdist_backward_out(const at::Tensor & grad, const at::Tensor & x1, const at::Tensor & x2, double p, const at::Tensor & cdist, at::Tensor & out); // {"schema": "aten::_cdist_backward.out(Tensor grad, Tensor x1, Tensor x2, float p, Tensor cdist, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _pdist_forward_out(const at::Tensor & self, double p, at::Tensor & out); // {"schema": "aten::_pdist_forward.out(Tensor self, float p=2, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _pdist_backward_out(const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist, at::Tensor & out); // {"schema": "aten::_pdist_backward.out(Tensor grad, Tensor self, float p, Tensor pdist, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & pixel_shuffle_out(const at::Tensor & self, int64_t upscale_factor, at::Tensor & out); // {"schema": "aten::pixel_shuffle.out(Tensor self, int upscale_factor, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & pixel_unshuffle_out(const at::Tensor & self, int64_t downscale_factor, at::Tensor & out); // {"schema": "aten::pixel_unshuffle.out(Tensor self, int downscale_factor, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & channel_shuffle_out(const at::Tensor & self, c10::SymInt groups, at::Tensor & out); // {"schema": "aten::channel_shuffle.out(Tensor self, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _pin_memory_out(const at::Tensor & self, ::std::optional<at::Device> device, at::Tensor & out); // {"schema": "aten::_pin_memory.out(Tensor self, Device? device=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & scalar_tensor_out(const at::Scalar & s, at::Tensor & out); // {"schema": "aten::scalar_tensor.out(Scalar s, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & rand_like_out(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::rand_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & rand_like_out(const at::Tensor & self, ::std::optional<at::Generator> generator, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::rand_like.generator_out(Tensor self, *, Generator? generator, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & randint_like_out(const at::Tensor & self, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::randint_like.out(Tensor self, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & randint_like_out(const at::Tensor & self, c10::SymInt high, ::std::optional<at::Generator> generator, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::randint_like.generator_out(Tensor self, SymInt high, *, Generator? generator, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & randint_like_out(const at::Tensor & self, const at::Tensor & high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::randint_like.Tensor_out(Tensor self, Tensor high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & randint_like_out(const at::Tensor & self, const at::Tensor & high, ::std::optional<at::Generator> generator, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::randint_like.Tensor_generator_out(Tensor self, Tensor high, *, Generator? generator, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & randint_like_out(const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::randint_like.low_dtype_out(Tensor self, SymInt low, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & randint_like_out(const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::Generator> generator, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::randint_like.low_generator_dtype_out(Tensor self, SymInt low, SymInt high, *, Generator? generator, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & randn_like_out(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::randn_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & randn_like_out(const at::Tensor & self, ::std::optional<at::Generator> generator, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::randn_like.generator_out(Tensor self, *, Generator? generator, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & repeat_out(const at::Tensor & self, c10::SymIntArrayRef repeats, at::Tensor & out); // {"schema": "aten::repeat.out(Tensor self, SymInt[] repeats, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & repeat_interleave_out(const at::Tensor & repeats, ::std::optional<c10::SymInt> output_size, at::Tensor & out); // {"schema": "aten::repeat_interleave.Tensor_out(Tensor repeats, *, SymInt? output_size=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _mkldnn_reshape_out(const at::Tensor & self, at::IntArrayRef shape, at::Tensor & out); // {"schema": "aten::_mkldnn_reshape.out(Tensor self, int[] shape, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & relu_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::relu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & select_backward_out(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index, at::Tensor & out); // {"schema": "aten::select_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & celu_out(const at::Tensor & self, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::celu.out(Tensor self, Scalar alpha=1.0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & slice_backward_out(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step, at::Tensor & out); // {"schema": "aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & slice_scatter_out(const at::Tensor & self, const at::Tensor & src, int64_t dim, ::std::optional<c10::SymInt> start, ::std::optional<c10::SymInt> end, c10::SymInt step, at::Tensor & out); // {"schema": "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!)", "dispatch": "True", "default": "True"}
at::Tensor & select_scatter_out(const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::SymInt index, at::Tensor & out); // {"schema": "aten::select_scatter.out(Tensor self, Tensor src, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & diagonal_scatter_out(const at::Tensor & self, const at::Tensor & src, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out); // {"schema": "aten::diagonal_scatter.out(Tensor self, Tensor src, int offset=0, int dim1=0, int dim2=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & as_strided_scatter_out(const at::Tensor & self, const at::Tensor & src, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset, at::Tensor & out); // {"schema": "aten::as_strided_scatter.out(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
void unsafe_split_out(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out); // {"schema": "aten::unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void unsafe_split_with_sizes_out(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out); // {"schema": "aten::unsafe_split_with_sizes.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
at::Tensor & sum_out(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::sum.out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> std_mean_out(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::std_mean.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
at::Tensor & prod_out(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::prod.out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _mkldnn_transpose_out(const at::Tensor & self, int64_t dim0, int64_t dim1, at::Tensor & out); // {"schema": "aten::_mkldnn_transpose.out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & flip_out(const at::Tensor & self, at::IntArrayRef dims, at::Tensor & out); // {"schema": "aten::flip.out(Tensor self, int[] dims, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & roll_out(const at::Tensor & self, c10::SymIntArrayRef shifts, at::IntArrayRef dims, at::Tensor & out); // {"schema": "aten::roll.out(Tensor self, SymInt[1] shifts, int[1] dims=[], *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & rot90_out(const at::Tensor & self, int64_t k, at::IntArrayRef dims, at::Tensor & out); // {"schema": "aten::rot90.out(Tensor self, int k=1, int[] dims=[0,1], *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _transform_bias_rescale_qkv_out(const at::Tensor & qkv, const at::Tensor & qkv_bias, int64_t num_heads, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::_transform_bias_rescale_qkv.out(Tensor qkv, Tensor qkv_bias, int num_heads, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
at::Tensor & _nested_tensor_from_mask_out(const at::Tensor & t, const at::Tensor & mask, bool mask_check, at::Tensor & out); // {"schema": "aten::_nested_tensor_from_mask.out(Tensor t, Tensor mask, bool mask_check=True, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _nested_from_padded_out(const at::Tensor & padded, const at::Tensor & cpu_nested_shape_example, bool fuse_transform_0213, at::Tensor & out); // {"schema": "aten::_nested_from_padded.out(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _nested_tensor_size_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::_nested_tensor_size.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _nested_tensor_strides_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::_nested_tensor_strides.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _nested_tensor_storage_offsets_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::_nested_tensor_storage_offsets.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _nested_from_padded_and_nested_example_out(const at::Tensor & padded, const at::Tensor & nt_example, at::Tensor & out); // {"schema": "aten::_nested_from_padded_and_nested_example.out(Tensor padded, Tensor nt_example, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _nested_view_from_buffer_copy_out(const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets, at::Tensor & out); // {"schema": "aten::_nested_view_from_buffer_copy.out(Tensor self, Tensor nested_size, Tensor nested_strides, Tensor offsets, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _nested_view_from_jagged_copy_out(const at::Tensor & self, const at::Tensor & offsets, const at::Tensor & dummy, const ::std::optional<at::Tensor> & lengths, int64_t ragged_idx, const ::std::optional<at::Tensor> & min_seqlen, const ::std::optional<at::Tensor> & max_seqlen, at::Tensor & out); // {"schema": "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!)", "dispatch": "True", "default": "True"}
at::Tensor & _nested_get_values_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::_nested_get_values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _trilinear_out(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 & out); // {"schema": "aten::_trilinear.out(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> _unique_out(const at::Tensor & self, bool sorted, bool return_inverse, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::_unique.out(Tensor self, bool sorted=True, bool return_inverse=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_out(const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::unique_dim.out(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_consecutive_out(const at::Tensor & self, bool return_inverse, bool return_counts, ::std::optional<int64_t> dim, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::unique_consecutive.out(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_consecutive_out(const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::unique_dim_consecutive.out(Tensor self, int dim, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _unique2_out(const at::Tensor & self, bool sorted, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::_unique2.out(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
at::Tensor & _unsafe_view_out(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out); // {"schema": "aten::_unsafe_view.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> var_mean_out(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::var_mean.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> _weight_norm_interface_out(const at::Tensor & v, const at::Tensor & g, int64_t dim, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::_weight_norm_interface.out(Tensor v, Tensor g, int dim=0, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> _weight_norm_interface_backward_out(const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::_weight_norm_interface_backward.out(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
at::Tensor & _efficientzerotensor_out(c10::SymIntArrayRef size, at::Tensor & out); // {"schema": "aten::_efficientzerotensor.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & zeros_like_out(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::zeros_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _standard_gamma_grad_out(const at::Tensor & self, const at::Tensor & output, at::Tensor & out); // {"schema": "aten::_standard_gamma_grad.out(Tensor self, Tensor output, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _standard_gamma_out(const at::Tensor & self, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::_standard_gamma.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _philox_normal_out(const at::Tensor & self, const at::Tensor & key, double mean, double std, at::Tensor & out); // {"schema": "aten::_philox_normal.out(Tensor self, Tensor key, float mean=0, float std=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor _philox_normal(const at::Tensor & self, const at::Tensor & key, double mean, double std); // {"schema": "aten::_philox_normal(Tensor self, Tensor key, float mean=0, float std=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _philox_uniform_out(const at::Tensor & self, const at::Tensor & key, double low, double high, at::Tensor & out); // {"schema": "aten::_philox_uniform.out(Tensor self, Tensor key, float low=0, float high=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor _philox_uniform(const at::Tensor & self, const at::Tensor & key, double low, double high); // {"schema": "aten::_philox_uniform(Tensor self, Tensor key, float low=0, float high=1) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _dirichlet_grad_out(const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total, at::Tensor & out); // {"schema": "aten::_dirichlet_grad.out(Tensor x, Tensor alpha, Tensor total, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _sample_dirichlet_out(const at::Tensor & self, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::_sample_dirichlet.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & poisson_out(const at::Tensor & self, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::poisson.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & binomial_out(const at::Tensor & count, const at::Tensor & prob, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::binomial.out(Tensor count, Tensor prob, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & native_norm_out(const at::Tensor & self, const at::Scalar & p, at::Tensor & out); // {"schema": "aten::native_norm.out(Tensor self, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & native_norm_out(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::native_norm.ScalarOpt_dim_dtype_out(Tensor self, Scalar? p, int[1] dim, bool keepdim, ScalarType? dtype, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _batch_norm_with_update_functional(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum, double eps); // {"schema": "aten::_batch_norm_with_update_functional(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor, Tensor running_mean_out, Tensor running_var_out)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _batch_norm_no_update_out(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3); // {"schema": "aten::_batch_norm_no_update.out(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, float momentum, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))", "dispatch": "True", "default": "True"}
at::Tensor & _sparse_sum_out(const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out); // {"schema": "aten::_sparse_sum.dim_out(Tensor self, int[1] dim, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _sparse_sum_backward_out(const at::Tensor & grad, const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out); // {"schema": "aten::_sparse_sum_backward.out(Tensor grad, Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _sparse_csr_sum_out(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::_sparse_csr_sum.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _sparse_csr_prod_out(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::_sparse_csr_prod.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _sparse_softmax_out(const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out); // {"schema": "aten::_sparse_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _sparse_softmax_backward_data_out(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self, at::Tensor & out); // {"schema": "aten::_sparse_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _sparse_log_softmax_out(const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out); // {"schema": "aten::_sparse_log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _sparse_log_softmax_backward_data_out(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self, at::Tensor & out); // {"schema": "aten::_sparse_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _spdiags_out(const at::Tensor & diagonals, const at::Tensor & offsets, at::IntArrayRef shape, ::std::optional<at::Layout> layout, at::Tensor & out); // {"schema": "aten::_spdiags.out(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & norm_out(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::ScalarType dtype, at::Tensor & out); // {"schema": "aten::norm.ScalarOpt_dtype_out(Tensor self, Scalar? p, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & norm_out(const at::Tensor & self, const at::Scalar & p, at::Tensor & out); // {"schema": "aten::norm.Scalar_out(Tensor self, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & clone_out(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::clone.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
const at::Tensor & resize_as_out(const at::Tensor & self, const at::Tensor & the_template, ::std::optional<at::MemoryFormat> memory_format, const at::Tensor & out); // {"schema": "aten::resize_as.out(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor resize_as(const at::Tensor & self, const at::Tensor & the_template, ::std::optional<at::MemoryFormat> memory_format); // {"schema": "aten::resize_as(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor", "dispatch": "True", "default": "True"}
const at::Tensor & resize_as_sparse_out(const at::Tensor & self, const at::Tensor & the_template, const at::Tensor & out); // {"schema": "aten::resize_as_sparse.out(Tensor self, Tensor the_template, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor resize_as_sparse(const at::Tensor & self, const at::Tensor & the_template); // {"schema": "aten::resize_as_sparse(Tensor self, Tensor the_template) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & zero_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::zero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor zero(const at::Tensor & self); // {"schema": "aten::zero(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & sub_out(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::sub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & rsub_out(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::rsub.Tensor_out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & rsub_out(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::rsub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _sparse_addmm_out(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out); // {"schema": "aten::_sparse_addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & sparse_coo_tensor_out(at::IntArrayRef size, at::Tensor & out); // {"schema": "aten::sparse_coo_tensor.size_out(int[] size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _sparse_coo_tensor_with_dims_out(int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, at::Tensor & out); // {"schema": "aten::_sparse_coo_tensor_with_dims.out(int sparse_dim, int dense_dim, int[] size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _sparse_coo_tensor_with_dims_and_tensors_out(int64_t sparse_dim, int64_t dense_dim, c10::SymIntArrayRef size, const at::Tensor & indices, const at::Tensor & values, ::std::optional<bool> is_coalesced, at::Tensor & out); // {"schema": "aten::_sparse_coo_tensor_with_dims_and_tensors.out(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, bool? is_coalesced=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
const at::Tensor & sparse_resize_out(const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim, const at::Tensor & out); // {"schema": "aten::sparse_resize.out(Tensor self, int[] size, int sparse_dim, int dense_dim, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor sparse_resize(const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim); // {"schema": "aten::sparse_resize(Tensor self, int[] size, int sparse_dim, int dense_dim) -> Tensor", "dispatch": "True", "default": "True"}
const at::Tensor & sparse_resize_and_clear_out(const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim, const at::Tensor & out); // {"schema": "aten::sparse_resize_and_clear.out(Tensor self, int[] size, int sparse_dim, int dense_dim, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor sparse_resize_and_clear(const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim); // {"schema": "aten::sparse_resize_and_clear(Tensor self, int[] size, int sparse_dim, int dense_dim) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & sparse_mask_out(const at::Tensor & self, const at::Tensor & mask, at::Tensor & out); // {"schema": "aten::sparse_mask.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _sparse_mask_projection_out(const at::Tensor & self, const at::Tensor & mask, bool accumulate_matches, at::Tensor & out); // {"schema": "aten::_sparse_mask_projection.out(Tensor self, Tensor mask, bool accumulate_matches=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _to_dense_out(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<bool> masked_grad, at::Tensor & out); // {"schema": "aten::_to_dense.out(Tensor self, ScalarType? dtype=None, bool? masked_grad=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _coalesce_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::_coalesce.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _coalesced_out(const at::Tensor & self, bool coalesced, at::Tensor & out); // {"schema": "aten::_coalesced.out(Tensor self, bool coalesced, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor _coalesced(const at::Tensor & self, bool coalesced); // {"schema": "aten::_coalesced(Tensor self, bool coalesced) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & copy_sparse_to_sparse_out(const at::Tensor & self, const at::Tensor & src, bool non_blocking, at::Tensor & out); // {"schema": "aten::copy_sparse_to_sparse.out(Tensor self, Tensor src, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor copy_sparse_to_sparse(const at::Tensor & self, const at::Tensor & src, bool non_blocking); // {"schema": "aten::copy_sparse_to_sparse(Tensor self, Tensor src, bool non_blocking=False) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & _to_sparse_out(const at::Tensor & self, int64_t sparse_dim, at::Tensor & out); // {"schema": "aten::_to_sparse.sparse_dim_out(Tensor self, int sparse_dim, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _to_sparse_out(const at::Tensor & self, ::std::optional<at::Layout> layout, at::OptionalIntArrayRef blocksize, ::std::optional<int64_t> dense_dim, at::Tensor & out); // {"schema": "aten::_to_sparse.out(Tensor self, *, Layout? layout=None, int[2]? blocksize=None, int? dense_dim=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _to_sparse_csr_out(const at::Tensor & self, ::std::optional<int64_t> dense_dim, at::Tensor & out); // {"schema": "aten::_to_sparse_csr.out(Tensor self, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _to_sparse_csc_out(const at::Tensor & self, ::std::optional<int64_t> dense_dim, at::Tensor & out); // {"schema": "aten::_to_sparse_csc.out(Tensor self, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _to_sparse_bsr_out(const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim, at::Tensor & out); // {"schema": "aten::_to_sparse_bsr.out(Tensor self, int[2] blocksize, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _to_sparse_bsc_out(const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim, at::Tensor & out); // {"schema": "aten::_to_sparse_bsc.out(Tensor self, int[2] blocksize, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & to_mkldnn_out(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out); // {"schema": "aten::to_mkldnn.out(Tensor self, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & mkldnn_reorder_conv2d_weight_out(const at::Tensor & self, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::OptionalSymIntArrayRef input_size, at::Tensor & out); // {"schema": "aten::mkldnn_reorder_conv2d_weight.out(Tensor self, SymInt[2] padding=0, SymInt[2] stride=1, SymInt[2] dilation=1, SymInt groups=1, SymInt[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & mkldnn_reorder_conv3d_weight_out(const at::Tensor & self, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::OptionalSymIntArrayRef input_size, at::Tensor & out); // {"schema": "aten::mkldnn_reorder_conv3d_weight.out(Tensor self, SymInt[3] padding=0, SymInt[3] stride=1, SymInt[3] dilation=1, SymInt groups=1, SymInt[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & quantize_per_tensor_dynamic_out(const at::Tensor & self, at::ScalarType dtype, bool reduce_range, at::Tensor & out); // {"schema": "aten::quantize_per_tensor_dynamic.out(Tensor self, ScalarType dtype, bool reduce_range, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & quantize_per_tensor_out(const at::Tensor & self, double scale, int64_t zero_point, at::ScalarType dtype, at::Tensor & out); // {"schema": "aten::quantize_per_tensor.out(Tensor self, float scale, int zero_point, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & quantize_per_tensor_out(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, at::ScalarType dtype, at::Tensor & out); // {"schema": "aten::quantize_per_tensor.tensor_qparams_out(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
void quantize_per_tensor_out(at::TensorList tensors, const at::Tensor & scales, const at::Tensor & zero_points, at::ScalarType dtype, at::TensorList out); // {"schema": "aten::quantize_per_tensor.tensors_out(Tensor[] tensors, Tensor scales, Tensor zero_points, ScalarType dtype, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
at::Tensor & quantize_per_channel_out(const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype, at::Tensor & out); // {"schema": "aten::quantize_per_channel.out(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & dequantize_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::dequantize.self_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
void dequantize_out(at::TensorList tensors, at::TensorList out); // {"schema": "aten::dequantize.tensors_out(Tensor[] tensors, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
at::Tensor & q_per_channel_scales_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::q_per_channel_scales.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & q_per_channel_zero_points_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::q_per_channel_zero_points.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & int_repr_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::int_repr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _make_per_tensor_quantized_tensor_out(const at::Tensor & self, double scale, int64_t zero_point, at::Tensor & out); // {"schema": "aten::_make_per_tensor_quantized_tensor.out(Tensor self, float scale, int zero_point, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _make_per_channel_quantized_tensor_out(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, at::Tensor & out); // {"schema": "aten::_make_per_channel_quantized_tensor.out(Tensor self, Tensor scale, Tensor zero_point, int axis, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> fake_quantize_per_tensor_affine_cachemask_out(const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::fake_quantize_per_tensor_affine_cachemask.out(Tensor self, float scale, int zero_point, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> _fake_quantize_per_tensor_affine_cachemask_tensor_qparams_out(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, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams.out(Tensor self, Tensor scale, Tensor zero_point, Tensor fake_quant_enabled, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
at::Tensor & _fake_quantize_learnable_per_tensor_affine_out(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 & out); // {"schema": "aten::_fake_quantize_learnable_per_tensor_affine.out(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> fake_quantize_per_channel_affine_cachemask_out(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 & out0, at::Tensor & out1); // {"schema": "aten::fake_quantize_per_channel_affine_cachemask.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
at::Tensor & _fake_quantize_learnable_per_channel_affine_out(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 & out); // {"schema": "aten::_fake_quantize_learnable_per_channel_affine.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> _fused_moving_avg_obs_fq_helper_out(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 & out0, at::Tensor & out1); // {"schema": "aten::_fused_moving_avg_obs_fq_helper.out(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(e!) out0, Tensor(f!) out1) -> (Tensor(e!), Tensor(f!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _fused_moving_avg_obs_fq_helper_functional(const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, const at::Tensor & running_min, const at::Tensor & running_max, const at::Tensor & scale, const 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); // {"schema": "aten::_fused_moving_avg_obs_fq_helper_functional(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor running_min, Tensor running_max, Tensor scale, Tensor 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, Tensor running_min_out, Tensor running_max_out, Tensor scale_out, Tensor zero_point_out)", "dispatch": "True", "default": "True"}
at::Tensor & _to_copy_out(const at::Tensor & self, bool non_blocking, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out); // {"schema": "aten::_to_copy.out(Tensor self, *, bool non_blocking=False, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _lstm_mps_out(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, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, at::Tensor & out5); // {"schema": "aten::_lstm_mps.out(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4, Tensor(f!) out5) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!), Tensor(f!))", "dispatch": "True", "default": "True"}
void lstm_mps_backward_out(const ::std::optional<at::Tensor> & grad_y, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, const at::Tensor & layersOutputs, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, at::Tensor & out0, at::TensorList out1, at::TensorList out2); // {"schema": "aten::lstm_mps_backward.out(Tensor? grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor layersOutputs, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, Tensor(a!) out0, Tensor(b!)[] out1, Tensor(c!)[] out2) -> ()", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_fused_lstm_cell_out(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & cx, const ::std::optional<at::Tensor> & input_bias, const ::std::optional<at::Tensor> & hidden_bias, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::_thnn_fused_lstm_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_fused_lstm_cell_backward_impl_out(const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::_thnn_fused_lstm_cell_backward_impl.out(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> _thnn_fused_gru_cell_out(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const ::std::optional<at::Tensor> & input_bias, const ::std::optional<at::Tensor> & hidden_bias, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::_thnn_fused_gru_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _thnn_fused_gru_cell_backward_out(const at::Tensor & grad_hy, const at::Tensor & workspace, bool has_bias, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4); // {"schema": "aten::_thnn_fused_gru_cell_backward.out(Tensor grad_hy, Tensor workspace, bool has_bias, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> _pack_padded_sequence_out(const at::Tensor & input, const at::Tensor & lengths, bool batch_first, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::_pack_padded_sequence.out(Tensor input, Tensor lengths, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
at::Tensor & set_out(const at::Tensor & self, at::Storage source, at::Tensor & out); // {"schema": "aten::set.source_Storage_out(Tensor self, Storage source, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor set(const at::Tensor & self, at::Storage source); // {"schema": "aten::set.source_Storage(Tensor self, Storage source) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & set_out(const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out); // {"schema": "aten::set.source_Storage_storage_offset_out(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[], *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor set(const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride); // {"schema": "aten::set.source_Storage_storage_offset(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & set_out(const at::Tensor & self, const at::Tensor & source, at::Tensor & out); // {"schema": "aten::set.source_Tensor_out(Tensor self, Tensor source, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor set(const at::Tensor & self, const at::Tensor & source); // {"schema": "aten::set.source_Tensor(Tensor self, Tensor source) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & set_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::set.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor set(const at::Tensor & self); // {"schema": "aten::set(Tensor self) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & lift_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::lift.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & lift_fresh_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::lift_fresh_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & masked_fill_out(const at::Tensor & self, const at::Tensor & mask, const at::Scalar & value, at::Tensor & out); // {"schema": "aten::masked_fill.Scalar_out(Tensor self, Tensor mask, Scalar value, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & masked_fill_out(const at::Tensor & self, const at::Tensor & mask, const at::Tensor & value, at::Tensor & out); // {"schema": "aten::masked_fill.Tensor_out(Tensor self, Tensor mask, Tensor value, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & masked_scatter_out(const at::Tensor & self, const at::Tensor & mask, const at::Tensor & source, at::Tensor & out); // {"schema": "aten::masked_scatter.out(Tensor self, Tensor mask, Tensor source, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _masked_softmax_out(const at::Tensor & self, const at::Tensor & mask, ::std::optional<int64_t> dim, ::std::optional<int64_t> mask_type, at::Tensor & out); // {"schema": "aten::_masked_softmax.out(Tensor self, Tensor mask, int? dim=None, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _masked_softmax_backward_out(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & mask, ::std::optional<int64_t> dim, at::Tensor & out); // {"schema": "aten::_masked_softmax_backward.out(Tensor grad_output, Tensor output, Tensor mask, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & put_out(const at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate, at::Tensor & out); // {"schema": "aten::put.out(Tensor self, Tensor index, Tensor source, bool accumulate=False, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & index_fill_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, at::Tensor & out); // {"schema": "aten::index_fill.int_Scalar_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & index_fill_out(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value, at::Tensor & out); // {"schema": "aten::index_fill.int_Tensor_out(Tensor self, int dim, Tensor index, Tensor value, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_and_out(const at::Scalar & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::bitwise_and.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_or_out(const at::Scalar & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::bitwise_or.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_xor_out(const at::Scalar & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::bitwise_xor.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & __lshift___out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::__lshift__.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & __lshift___out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::__lshift__.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_left_shift_out(const at::Scalar & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::bitwise_left_shift.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & __rshift___out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); // {"schema": "aten::__rshift__.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & __rshift___out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::__rshift__.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & bitwise_right_shift_out(const at::Scalar & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::bitwise_right_shift.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & random_out(const at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::random.from_out(Tensor self, int from, int? to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor random(const at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator); // {"schema": "aten::random.from(Tensor self, int from, int? to, *, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & random_out(const at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::random.to_out(Tensor self, int to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor random(const at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator); // {"schema": "aten::random.to(Tensor self, int to, *, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & random_out(const at::Tensor & self, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::random.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor random(const at::Tensor & self, ::std::optional<at::Generator> generator); // {"schema": "aten::random(Tensor self, *, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & uniform_out(const at::Tensor & self, double from, double to, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::uniform.out(Tensor self, float from=0, float to=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor uniform(const at::Tensor & self, double from, double to, ::std::optional<at::Generator> generator); // {"schema": "aten::uniform(Tensor self, float from=0, float to=1, *, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & cauchy_out(const at::Tensor & self, double median, double sigma, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::cauchy.out(Tensor self, float median=0, float sigma=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor cauchy(const at::Tensor & self, double median, double sigma, ::std::optional<at::Generator> generator); // {"schema": "aten::cauchy(Tensor self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & log_normal_out(const at::Tensor & self, double mean, double std, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::log_normal.out(Tensor self, float mean=1, float std=2, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor log_normal(const at::Tensor & self, double mean, double std, ::std::optional<at::Generator> generator); // {"schema": "aten::log_normal(Tensor self, float mean=1, float std=2, *, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & exponential_out(const at::Tensor & self, double lambd, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::exponential.out(Tensor self, float lambd=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor exponential(const at::Tensor & self, double lambd, ::std::optional<at::Generator> generator); // {"schema": "aten::exponential(Tensor self, float lambd=1, *, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & geometric_out(const at::Tensor & self, double p, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::geometric.out(Tensor self, float p, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor geometric(const at::Tensor & self, double p, ::std::optional<at::Generator> generator); // {"schema": "aten::geometric(Tensor self, float p, *, Generator? generator=None) -> Tensor", "dispatch": "True", "default": "True"}
at::Tensor & tril_indices_out(int64_t row, int64_t col, int64_t offset, at::Tensor & out); // {"schema": "aten::tril_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & triu_indices_out(int64_t row, int64_t col, int64_t offset, at::Tensor & out); // {"schema": "aten::triu_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & trace_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::trace.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _cholesky_solve_helper_out(const at::Tensor & self, const at::Tensor & A, bool upper, at::Tensor & out); // {"schema": "aten::_cholesky_solve_helper.out(Tensor self, Tensor A, bool upper, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & dist_out(const at::Tensor & self, const at::Tensor & other, const at::Scalar & p, at::Tensor & out); // {"schema": "aten::dist.out(Tensor self, Tensor other, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
void _histogramdd_bin_edges_out(const at::Tensor & self, at::IntArrayRef bins, ::std::optional<at::ArrayRef<double>> range, const ::std::optional<at::Tensor> & weight, bool density, at::TensorList out); // {"schema": "aten::_histogramdd_bin_edges.out(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
at::Tensor & _histogramdd_from_bin_cts_out(const at::Tensor & self, at::IntArrayRef bins, ::std::optional<at::ArrayRef<double>> range, const ::std::optional<at::Tensor> & weight, bool density, at::Tensor & out); // {"schema": "aten::_histogramdd_from_bin_cts.out(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _histogramdd_from_bin_tensors_out(const at::Tensor & self, at::TensorList bins, const ::std::optional<at::Tensor> & weight, bool density, at::Tensor & out); // {"schema": "aten::_histogramdd_from_bin_tensors.out(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & remainder_out(const at::Scalar & self, const at::Tensor & other, at::Tensor & out); // {"schema": "aten::remainder.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & unfold_backward_out(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out); // {"schema": "aten::unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & normal_out(const at::Tensor & self, double mean, double std, ::std::optional<at::Generator> generator, at::Tensor & out); // {"schema": "aten::normal.out(Tensor self, float mean=0, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
void _amp_foreach_non_finite_check_and_unscale_out(at::TensorList self, at::Tensor & found_inf, const at::Tensor & inv_scale, at::TensorList out); // {"schema": "aten::_amp_foreach_non_finite_check_and_unscale.out(Tensor[] self, Tensor(b!) found_inf, Tensor inv_scale, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
::std::tuple<::std::vector<at::Tensor>,at::Tensor> _amp_foreach_non_finite_check_and_unscale(at::TensorList self, const at::Tensor & found_inf, const at::Tensor & inv_scale); // {"schema": "aten::_amp_foreach_non_finite_check_and_unscale(Tensor[] self, Tensor found_inf, Tensor inv_scale) -> (Tensor[] self_out, Tensor found_inf_out)", "dispatch": "True", "default": "True"}
at::Tensor & _amp_update_scale_out(const 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 & out); // {"schema": "aten::_amp_update_scale.out(Tensor self, Tensor(b!) growth_tracker, Tensor found_inf, float scale_growth_factor, float scale_backoff_factor, int growth_interval, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> _amp_update_scale(const at::Tensor & self, const at::Tensor & growth_tracker, const at::Tensor & found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval); // {"schema": "aten::_amp_update_scale(Tensor self, Tensor growth_tracker, Tensor found_inf, float scale_growth_factor, float scale_backoff_factor, int growth_interval) -> (Tensor, Tensor growth_tracker_out)", "dispatch": "True", "default": "True"}
void _foreach_add_out(at::TensorList self, const at::Scalar & scalar, at::TensorList out); // {"schema": "aten::_foreach_add.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_add_out(at::TensorList self, at::TensorList other, const at::Scalar & alpha, at::TensorList out); // {"schema": "aten::_foreach_add.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_add_out(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out); // {"schema": "aten::_foreach_add.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_add_out(at::TensorList self, const at::Tensor & other, const at::Scalar & alpha, at::TensorList out); // {"schema": "aten::_foreach_add.Tensor_out(Tensor[] self, Tensor other, *, Scalar alpha=1, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_sub_out(at::TensorList self, const at::Scalar & scalar, at::TensorList out); // {"schema": "aten::_foreach_sub.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_sub_out(at::TensorList self, at::TensorList other, const at::Scalar & alpha, at::TensorList out); // {"schema": "aten::_foreach_sub.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_sub_out(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out); // {"schema": "aten::_foreach_sub.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_mul_out(at::TensorList self, const at::Scalar & scalar, at::TensorList out); // {"schema": "aten::_foreach_mul.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_mul_out(at::TensorList self, at::TensorList other, at::TensorList out); // {"schema": "aten::_foreach_mul.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_mul_out(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out); // {"schema": "aten::_foreach_mul.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_mul_out(at::TensorList self, const at::Tensor & other, at::TensorList out); // {"schema": "aten::_foreach_mul.Tensor_out(Tensor[] self, Tensor other, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_div_out(at::TensorList self, const at::Scalar & scalar, at::TensorList out); // {"schema": "aten::_foreach_div.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_div_out(at::TensorList self, at::TensorList other, at::TensorList out); // {"schema": "aten::_foreach_div.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_div_out(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out); // {"schema": "aten::_foreach_div.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_div_out(at::TensorList self, const at::Tensor & other, at::TensorList out); // {"schema": "aten::_foreach_div.Tensor_out(Tensor[] self, Tensor other, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_clamp_max_out(at::TensorList self, const at::Scalar & scalar, at::TensorList out); // {"schema": "aten::_foreach_clamp_max.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_clamp_max_out(at::TensorList self, at::TensorList other, at::TensorList out); // {"schema": "aten::_foreach_clamp_max.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_clamp_max_out(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out); // {"schema": "aten::_foreach_clamp_max.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_clamp_min_out(at::TensorList self, const at::Scalar & scalar, at::TensorList out); // {"schema": "aten::_foreach_clamp_min.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_clamp_min_out(at::TensorList self, at::TensorList other, at::TensorList out); // {"schema": "aten::_foreach_clamp_min.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_clamp_min_out(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out); // {"schema": "aten::_foreach_clamp_min.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_maximum_out(at::TensorList self, const at::Scalar & scalar, at::TensorList out); // {"schema": "aten::_foreach_maximum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_maximum_out(at::TensorList self, at::TensorList other, at::TensorList out); // {"schema": "aten::_foreach_maximum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_maximum_out(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out); // {"schema": "aten::_foreach_maximum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_minimum_out(at::TensorList self, const at::Scalar & scalar, at::TensorList out); // {"schema": "aten::_foreach_minimum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_minimum_out(at::TensorList self, at::TensorList other, at::TensorList out); // {"schema": "aten::_foreach_minimum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_minimum_out(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out); // {"schema": "aten::_foreach_minimum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_addcdiv_out(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value, at::TensorList out); // {"schema": "aten::_foreach_addcdiv.Scalar_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_addcdiv_out(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars, at::TensorList out); // {"schema": "aten::_foreach_addcdiv.ScalarList_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_addcdiv_out(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars, at::TensorList out); // {"schema": "aten::_foreach_addcdiv.Tensor_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_addcmul_out(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value, at::TensorList out); // {"schema": "aten::_foreach_addcmul.Scalar_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_addcmul_out(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars, at::TensorList out); // {"schema": "aten::_foreach_addcmul.ScalarList_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_addcmul_out(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars, at::TensorList out); // {"schema": "aten::_foreach_addcmul.Tensor_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_abs_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_abs.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_acos_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_acos.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_asin_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_asin.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_atan_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_atan.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_ceil_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_ceil.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_cos_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_cos.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_cosh_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_cosh.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_erf_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_erf.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_erfc_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_erfc.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_exp_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_exp.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_expm1_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_expm1.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_floor_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_floor.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_frac_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_frac.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_lerp_out(at::TensorList self, at::TensorList tensors1, at::TensorList weights, at::TensorList out); // {"schema": "aten::_foreach_lerp.List_out(Tensor[] self, Tensor[] tensors1, Tensor[] weights, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_lerp_out(at::TensorList self, at::TensorList tensors1, const at::Scalar & weight, at::TensorList out); // {"schema": "aten::_foreach_lerp.Scalar_out(Tensor[] self, Tensor[] tensors1, Scalar weight, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_lerp_out(at::TensorList self, at::TensorList tensors1, at::ArrayRef<at::Scalar> weight, at::TensorList out); // {"schema": "aten::_foreach_lerp.ScalarList_out(Tensor[] self, Tensor[] tensors1, Scalar[] weight, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_lgamma_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_lgamma.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_log_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_log.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_log10_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_log10.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_log1p_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_log1p.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_log2_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_log2.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_max_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_max.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_neg_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_neg.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_norm_out(at::TensorList self, const at::Scalar & ord, ::std::optional<at::ScalarType> dtype, at::TensorList out); // {"schema": "aten::_foreach_norm.Scalar_out(Tensor[] self, Scalar ord=2, ScalarType? dtype=None, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_powsum_out(at::TensorList self, const at::Scalar & ord, ::std::optional<at::ScalarType> dtype, at::TensorList out); // {"schema": "aten::_foreach_powsum.Scalar_out(Tensor[] self, Scalar ord=2, ScalarType? dtype=None, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_pow_out(at::TensorList self, at::TensorList exponent, at::TensorList out); // {"schema": "aten::_foreach_pow.List_out(Tensor[] self, Tensor[] exponent, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_pow_out(at::TensorList self, const at::Scalar & exponent, at::TensorList out); // {"schema": "aten::_foreach_pow.Scalar_out(Tensor[] self, Scalar exponent, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_pow_out(at::TensorList self, at::ArrayRef<at::Scalar> exponent, at::TensorList out); // {"schema": "aten::_foreach_pow.ScalarList_out(Tensor[] self, Scalar[] exponent, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_reciprocal_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_reciprocal.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_round_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_round.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_rsqrt_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_rsqrt.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_sigmoid_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_sigmoid.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_sign_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_sign.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_sin_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_sin.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_sinh_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_sinh.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_sqrt_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_sqrt.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_tan_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_tan.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_tanh_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_tanh.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_trunc_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_trunc.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_zero_out(at::TensorList self, at::TensorList out); // {"schema": "aten::_foreach_zero.out(Tensor[] self, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
::std::vector<at::Tensor> _foreach_zero(at::TensorList self); // {"schema": "aten::_foreach_zero(Tensor[] self) -> Tensor[] self_out", "dispatch": "True", "default": "True"}
void _foreach_clone_out(at::TensorList self, ::std::optional<at::MemoryFormat> memory_format, at::TensorList out); // {"schema": "aten::_foreach_clone.out(Tensor[] self, *, MemoryFormat? memory_format=None, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
void _foreach_copy_out(at::TensorList self, at::TensorList src, bool non_blocking, at::TensorList out); // {"schema": "aten::_foreach_copy.out(Tensor[] self, Tensor[] src, bool non_blocking=False, *, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
at::Tensor & bucketize_out(const at::Scalar & self, const at::Tensor & boundaries, bool out_int32, bool right, at::Tensor & out); // {"schema": "aten::bucketize.Scalar_out(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & glu_jvp_out(const at::Tensor & glu, const at::Tensor & x, const at::Tensor & dx, int64_t dim, at::Tensor & out); // {"schema": "aten::glu_jvp.out(Tensor glu, Tensor x, Tensor dx, int dim, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & glu_backward_jvp_out(const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim, at::Tensor & out); // {"schema": "aten::glu_backward_jvp.out(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & hardswish_backward_out(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out); // {"schema": "aten::hardswish_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor,at::Tensor> rrelu_with_noise_functional(const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, ::std::optional<at::Generator> generator); // {"schema": "aten::rrelu_with_noise_functional(Tensor self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> (Tensor, Tensor noise_out)", "dispatch": "True", "default": "True"}
at::Tensor & rrelu_with_noise_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, bool self_is_result, at::Tensor & out); // {"schema": "aten::rrelu_with_noise_backward.out(Tensor grad_output, Tensor self, Tensor noise, Scalar lower, Scalar upper, bool training, bool self_is_result, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & mkldnn_adaptive_avg_pool2d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out); // {"schema": "aten::mkldnn_adaptive_avg_pool2d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _adaptive_avg_pool2d_out(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out); // {"schema": "aten::_adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _adaptive_avg_pool2d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out); // {"schema": "aten::_adaptive_avg_pool2d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _adaptive_avg_pool3d_out(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out); // {"schema": "aten::_adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _adaptive_avg_pool3d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out); // {"schema": "aten::_adaptive_avg_pool3d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & upsample_bilinear2d_out(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out); // {"schema": "aten::upsample_bilinear2d.vec_out(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & upsample_nearest2d_out(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out); // {"schema": "aten::upsample_nearest2d.vec_out(Tensor input, SymInt[]? output_size, float[]? scale_factors, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); // {"schema": "aten::_slow_conv2d_backward.output_mask_out(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))", "dispatch": "True", "default": "True"}
at::Tensor & conv_depthwise3d_out(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out); // {"schema": "aten::conv_depthwise3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & slow_conv_dilated2d_out(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out); // {"schema": "aten::slow_conv_dilated2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & slow_conv_dilated3d_out(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out); // {"schema": "aten::slow_conv_dilated3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & isinf_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::isinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & linalg_matrix_exp_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::linalg_matrix_exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _test_optional_intlist_out(const at::Tensor & values, at::OptionalIntArrayRef addends, at::Tensor & out); // {"schema": "aten::_test_optional_intlist.out(Tensor values, int[]? addends, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _test_optional_filled_intlist_out(const at::Tensor & values, at::OptionalIntArrayRef addends, at::Tensor & out); // {"schema": "aten::_test_optional_filled_intlist.out(Tensor values, int[2]? addends, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _test_optional_floatlist_out(const at::Tensor & values, ::std::optional<at::ArrayRef<double>> addends, at::Tensor & out); // {"schema": "aten::_test_optional_floatlist.out(Tensor values, float[]? addends, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _test_warn_in_autograd_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::_test_warn_in_autograd.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _test_autograd_multiple_dispatch_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::_test_autograd_multiple_dispatch.fullcoverage_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _test_autograd_multiple_dispatch_view_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::_test_autograd_multiple_dispatch_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & segment_reduce_out(const at::Tensor & data, c10::string_view reduce, const ::std::optional<at::Tensor> & lengths, const ::std::optional<at::Tensor> & indices, const ::std::optional<at::Tensor> & offsets, int64_t axis, bool unsafe, const ::std::optional<at::Scalar> & initial, at::Tensor & out); // {"schema": "aten::segment_reduce.out(Tensor data, str reduce, *, Tensor? lengths=None, Tensor? indices=None, Tensor? offsets=None, int axis=0, bool unsafe=False, Scalar? initial=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _segment_reduce_backward_out(const at::Tensor & grad, const at::Tensor & output, const at::Tensor & data, c10::string_view reduce, const ::std::optional<at::Tensor> & lengths, const ::std::optional<at::Tensor> & offsets, int64_t axis, const ::std::optional<at::Scalar> & initial, at::Tensor & out); // {"schema": "aten::_segment_reduce_backward.out(Tensor grad, Tensor output, Tensor data, str reduce, *, Tensor? lengths=None, Tensor? offsets=None, int axis=0, Scalar? initial=None, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _nested_tensor_from_tensor_list_out(at::TensorList list, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, at::Tensor & out); // {"schema": "aten::_nested_tensor_from_tensor_list.out(Tensor[] list, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _fw_primal_copy_out(const at::Tensor & self, int64_t level, at::Tensor & out); // {"schema": "aten::_fw_primal_copy.out(Tensor self, int level, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _make_dual_copy_out(const at::Tensor & primal, const at::Tensor & tangent, int64_t level, at::Tensor & out); // {"schema": "aten::_make_dual_copy.out(Tensor primal, Tensor tangent, int level, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & view_as_real_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::view_as_real_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & view_as_complex_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::view_as_complex_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _conj_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::_conj_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _neg_view_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::_neg_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & as_strided_copy_out(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset, at::Tensor & out); // {"schema": "aten::as_strided_copy.out(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _sparse_broadcast_to_copy_out(const at::Tensor & self, at::IntArrayRef size, at::Tensor & out); // {"schema": "aten::_sparse_broadcast_to_copy.out(Tensor self, int[] size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & diagonal_copy_out(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out); // {"schema": "aten::diagonal_copy.out(Tensor self, int offset=0, int dim1=0, int dim2=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & expand_copy_out(const at::Tensor & self, c10::SymIntArrayRef size, bool implicit, at::Tensor & out); // {"schema": "aten::expand_copy.out(Tensor self, SymInt[] size, *, bool implicit=False, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & permute_copy_out(const at::Tensor & self, at::IntArrayRef dims, at::Tensor & out); // {"schema": "aten::permute_copy.out(Tensor self, int[] dims, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _reshape_alias_copy_out(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out); // {"schema": "aten::_reshape_alias_copy.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & select_copy_out(const at::Tensor & self, int64_t dim, c10::SymInt index, at::Tensor & out); // {"schema": "aten::select_copy.int_out(Tensor self, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & detach_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::detach_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & slice_copy_out(const at::Tensor & self, int64_t dim, ::std::optional<c10::SymInt> start, ::std::optional<c10::SymInt> end, c10::SymInt step, at::Tensor & out); // {"schema": "aten::slice_copy.Tensor_out(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & squeeze_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::squeeze_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & squeeze_copy_out(const at::Tensor & self, int64_t dim, at::Tensor & out); // {"schema": "aten::squeeze_copy.dim_out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & squeeze_copy_out(const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out); // {"schema": "aten::squeeze_copy.dims_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & t_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::t_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & transpose_copy_out(const at::Tensor & self, int64_t dim0, int64_t dim1, at::Tensor & out); // {"schema": "aten::transpose_copy.int_out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & unsqueeze_copy_out(const at::Tensor & self, int64_t dim, at::Tensor & out); // {"schema": "aten::unsqueeze_copy.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _indices_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _values_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::_values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & indices_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & values_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & crow_indices_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::crow_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & col_indices_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::col_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & ccol_indices_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::ccol_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & row_indices_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::row_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & view_copy_out(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out); // {"schema": "aten::view_copy.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & view_copy_out(const at::Tensor & self, at::ScalarType dtype, at::Tensor & out); // {"schema": "aten::view_copy.dtype_out(Tensor self, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & unfold_copy_out(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step, at::Tensor & out); // {"schema": "aten::unfold_copy.out(Tensor self, int dimension, int size, int step, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & alias_copy_out(const at::Tensor & self, at::Tensor & out); // {"schema": "aten::alias_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & to_padded_tensor_out(const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size, at::Tensor & out); // {"schema": "aten::to_padded_tensor.out(Tensor self, float padding, SymInt[]? output_size=None, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _transformer_encoder_layer_fwd_out(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<at::Tensor> & mask, ::std::optional<int64_t> mask_type, at::Tensor & out); // {"schema": "aten::_transformer_encoder_layer_fwd.out(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(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
::std::tuple<at::Tensor &,at::Tensor &> _native_multi_head_attention_out(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<at::Tensor> & mask, bool need_weights, bool average_attn_weights, ::std::optional<int64_t> mask_type, at::Tensor & out0, at::Tensor & out1); // {"schema": "aten::_native_multi_head_attention.out(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(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))", "dispatch": "True", "default": "True"}
at::Tensor & _triton_scaled_dot_attention_out(const at::Tensor & q, const at::Tensor & k, const at::Tensor & v, double dropout_p, at::Tensor & out); // {"schema": "aten::_triton_scaled_dot_attention.out(Tensor q, Tensor k, Tensor v, float dropout_p=0.0, *, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _triton_multi_head_attention_out(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<at::Tensor> & mask, at::Tensor & out); // {"schema": "aten::_triton_multi_head_attention.out(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(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
at::Tensor & _foobar_out(const at::Tensor & self, bool arg1, bool arg2, bool arg3, at::Tensor & out); // {"schema": "aten::_foobar.out(Tensor self, bool arg1=True, bool arg2=True, *, bool arg3=True, Tensor(a!) out) -> Tensor(a!)", "dispatch": "True", "default": "True"}
void _fused_adam_out(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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf, at::TensorList out); // {"schema": "aten::_fused_adam.out(Tensor[] 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, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf); // {"schema": "aten::_fused_adam(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] 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) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out)", "dispatch": "True", "default": "True"}
void _fused_adam_out(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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf, at::TensorList out); // {"schema": "aten::_fused_adam.tensor_lr_out(Tensor[] 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, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf); // {"schema": "aten::_fused_adam.tensor_lr(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] 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) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out)", "dispatch": "True", "default": "True"}
void _fused_adamw_out(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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf, at::TensorList out); // {"schema": "aten::_fused_adamw.out(Tensor[] 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, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf); // {"schema": "aten::_fused_adamw(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] 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) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out)", "dispatch": "True", "default": "True"}
void _fused_adamw_out(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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf, at::TensorList out); // {"schema": "aten::_fused_adamw.tensor_lr_out(Tensor[] 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, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf); // {"schema": "aten::_fused_adamw.tensor_lr(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] 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) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out)", "dispatch": "True", "default": "True"}
void _fused_sgd_out(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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf, at::TensorList out); // {"schema": "aten::_fused_sgd.out(Tensor[] 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, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf); // {"schema": "aten::_fused_sgd(Tensor[] self, Tensor[] grads, Tensor[] 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) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] momentum_buffer_list_out)", "dispatch": "True", "default": "True"}
void _fused_sgd_out(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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf, at::TensorList out); // {"schema": "aten::_fused_sgd.tensor_lr_out(Tensor[] 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, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf); // {"schema": "aten::_fused_sgd.tensor_lr(Tensor[] self, Tensor[] grads, Tensor[] 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) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] momentum_buffer_list_out)", "dispatch": "True", "default": "True"}
void _fused_adagrad_out(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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf, at::TensorList out); // {"schema": "aten::_fused_adagrad.out(Tensor[] 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, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _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<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf); // {"schema": "aten::_fused_adagrad(Tensor[] self, Tensor[] grads, Tensor[] state_sums, Tensor[] state_steps, *, float lr, float lr_decay, float weight_decay, float eps, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] state_sums_out, Tensor[] state_steps_out)", "dispatch": "True", "default": "True"}
void _fused_adagrad_out(at::TensorList self, at::TensorList grads, at::TensorList state_sums, at::TensorList state_steps, const at::Tensor & lr, double lr_decay, double weight_decay, double eps, bool maximize, const ::std::optional<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf, at::TensorList out); // {"schema": "aten::_fused_adagrad.tensor_lr_out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] state_sums, Tensor[] state_steps, *, Tensor lr, float lr_decay, float weight_decay, float eps, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()", "dispatch": "True", "default": "True"}
::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _fused_adagrad(at::TensorList self, at::TensorList grads, at::TensorList state_sums, at::TensorList state_steps, const at::Tensor & lr, double lr_decay, double weight_decay, double eps, bool maximize, const ::std::optional<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf); // {"schema": "aten::_fused_adagrad.tensor_lr(Tensor[] self, Tensor[] grads, Tensor[] state_sums, Tensor[] state_steps, *, Tensor lr, float lr_decay, float weight_decay, float eps, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] state_sums_out)", "dispatch": "True", "default": "True"}

#else
#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
