/usr/local/lib64/python3.6/site-packages/torch/_C/
/usr/local/lib64/python3.6/site-packages/torch/_C
NameSizeModeActions
_autograd.pyi27440644editdlrm
_cudnn.pyi3780644editdlrm
_distributed_autograd.pyi9080644editdlrm
_distributed_c10d.pyi93760644editdlrm
_distributed_rpc.pyi56300644editdlrm
_distributed_rpc_testing.pyi11360644editdlrm
_functions.pyi3450644editdlrm
_nn.pyi20500644editdlrm
_nvtx.pyi1500644editdlrm
_onnx.pyi6460644editdlrm
_VariableFunctions.pyi1410260644editdlrm
__init__.pyi1039060644editdlrm
Edit: /usr/local/lib64/python3.6/site-packages/torch/_C/__init__.pyi (103906B)
# @generated from torch/_C/__init__.pyi.in import torch from torch.package import PackageExporter from torch import Tensor from enum import Enum from pathlib import Path from typing import ( Any, BinaryIO, Callable, ContextManager, Dict, Iterable, Iterator, List, NamedTuple, Optional, overload, Sequence, Tuple, TypeVar, Type, Union, Generic, Set, AnyStr) from typing_extensions import Literal from torch._six import inf from torch.types import _int, _float, _bool, _dtype, _device, _qscheme, _size, _layout, Device, Number, Storage import builtins # This module is defined in torch/csrc/Module.cpp from . import _nn as _nn from . import _onnx as _onnx from . import _VariableFunctions as _VariableFunctions T = TypeVar('T') # Defined in torch/csrc/Device.cpp class device: type: str # THPDevice_type index: _int # THPDevice_index def __get__(self, instance, owner=None) -> device: ... # THPDevice_pynew @overload def __init__(self, device: Union[_device, _int, str]) -> None: ... @overload def __init__(self, type: str, index: _int) -> None: ... def __reduce__(self) -> Tuple[Any, ...]: ... # THPDevice_reduce # Defined in torch/csrc/Stream.cpp class Stream: _cdata: _int # Stream handle device: device # The device of the stream ... # Defined in torch/csrc/Size.cpp class Size(Tuple[_int, ...]): # TODO: __reduce__ @overload def __getitem__(self: Size, key: _int) -> _int: ... @overload def __getitem__(self: Size, key: slice) -> Size: ... def numel(self: Size) -> _int: ... ... # Defined in torch/csrc/Dtype.cpp class dtype: # TODO: __reduce__ is_floating_point: _bool is_complex: _bool is_signed: _bool ... # Defined in torch/csrc/TypeInfo.cpp class iinfo: bits: _int min: _int max: _int dtype: str def __init__(self, dtype: _dtype) -> None: ... class finfo: bits: _int min: _float max: _float eps: _float tiny: _float resolution: _float dtype: str @overload def __init__(self, dtype: _dtype) -> None: ... @overload def __init__(self) -> None: ... float32: dtype = ... float: dtype = ... float64: dtype = ... double: dtype = ... float16: dtype = ... bfloat16: dtype = ... half: dtype = ... uint8: dtype = ... int8: dtype = ... int16: dtype = ... short: dtype = ... int32: dtype = ... int: dtype = ... int64: dtype = ... long: dtype = ... complex32: dtype = ... complex64: dtype = ... cfloat: dtype = ... complex128: dtype = ... cdouble: dtype = ... quint8: dtype = ... qint8: dtype = ... qint32: dtype = ... bool: dtype = ... quint4x2: dtype = ... # Defined in torch/csrc/Layout.cpp class layout: ... # Defined in torch/csrc/utils/disable_torch_function.cpp def DisableTorchFunction(): ... # Defined in torch/csrc/utils/tensor_layouts.cpp strided : layout = ... sparse_coo : layout = ... _mkldnn : layout = ... # Defined in torch/csrc/MemoryFormat.cpp class memory_format: ... # Defined in torch/csrc/utils/tensor_memoryformats.cpp contiguous_format: memory_format = ... channels_last: memory_format = ... channels_last_3d: memory_format = ... preserve_format: memory_format = ... # Defined in torch/csrc/QScheme.cpp class qscheme: ... # Defined in torch/csrc/utils/tensor_qschemes.h per_tensor_affine: qscheme = ... per_channel_affine: qscheme = ... per_tensor_symmetric: qscheme = ... per_channel_symmetric: qscheme = ... per_channel_affine_float_qparams: qscheme = ... # Defined in torch/csrc/autograd/python_function.cpp class _FunctionBase(object): ... # Defined in torch/csrc/autograd/python_legacy_variable.cpp class _LegacyVariableBase(object): def __init__( self, data: Optional[Tensor]=..., requires_grad: Optional[_bool]=..., volatile: Optional[_bool]=..., _grad_fn: Optional[_FunctionBase]=... ) -> None: ... # Defined in torch/csrc/jit/python/init.cpp class IODescriptor: ... class JITException: ... class Future(object): def __init__(self, devices: List[device]) -> None: ... def done(self) -> _bool: ... def value(self) -> Any: ... def wait(self) -> Any: ... def add_done_callback(self, callback: Callable) -> None: ... def then(self, callback: Callable) -> Future: ... def set_result(self, result: Any) -> None: ... def _set_unwrap_func(self, callback: Callable) -> None: ... def _jit_set_num_profiled_runs(num: _size) -> _size: ... # Defined in torch/csrc/jit/passes/xnnpack_rewrite.h class MobileOptimizerType: ... CONV_BN_FUSION: MobileOptimizerType INSERT_FOLD_PREPACK_OPS: MobileOptimizerType REMOVE_DROPOUT: MobileOptimizerType FUSE_ADD_RELU: MobileOptimizerType HOIST_CONV_PACKED_PARAMS: MobileOptimizerType def fork(*args: Any, **kwargs: Any) -> Future: ... def wait(fut: Future) -> Any: ... def _collect_all(futures: List[Future]) -> Future: ... def _set_print_stack_traces_on_fatal_signal(print: _bool) -> None: ... def unify_type_list(types: List[JitType]) -> JitType: ... def _freeze_module(module: ScriptModule, preserved_attrs: List[str] = [], freeze_interfaces: _bool = True, preserveParameters: _bool = True) -> ScriptModule: ... def _jit_pass_optimize_frozen_graph(Graph) -> None: ... def _jit_pass_fold_frozen_conv_bn(graph: Graph): ... def _jit_pass_fold_frozen_conv_add_or_sub(graph: Graph): ... def _jit_pass_fold_frozen_conv_mul_or_div(graph: Graph): ... def _jit_pass_fuse_frozen_conv_add_relu(graph: Graph): ... def _jit_pass_convert_frozen_ops_to_mkldnn(graph: Graph): ... def _jit_pass_remove_dropout(module: 'torch.jit.ScriptModule'): ... def _is_tracing() -> _bool: ... def _jit_init() -> _bool: ... def _jit_flatten(arg: Any) -> Tuple[List[Tensor], IODescriptor]: ... def _jit_unflatten(vars: List[Tensor], desc: IODescriptor) -> Any: ... def _jit_get_operation(op_name: str) -> Callable: ... def _jit_pass_optimize_for_mobile(module: 'torch.jit.ScriptModule', optimization_blocklist: Set[MobileOptimizerType], preserved_methods: List[AnyStr]) -> 'torch.jit.ScriptModule': ... def _clone_module_with_class(module: 'torch.jit.ScriptModule', ignored_methods: List[AnyStr], ignored_attributes: List[AnyStr]) -> 'torch.jit.ScriptModule': ... def _jit_pass_vulkan_optimize_for_mobile(module: 'torch.jit.ScriptModule', preserved_methods: List[AnyStr]) -> 'torch.jit.ScriptModule': ... def _jit_pass_metal_optimize_for_mobile(module: 'torch.jit.ScriptModule', preserved_methods: List[AnyStr]) -> 'torch.jit.ScriptModule': ... def _jit_pass_inline(Graph) -> None: ... def _jit_pass_constant_propagation(Graph) -> None: ... def _jit_pass_propagate_shapes_on_graph(Graph) -> None: ... def _jit_erase_non_input_shape_information(Graph) -> None: ... def _jit_get_schemas_for_operator(name :str) -> List[FunctionSchema]: ... def _jit_check_alias_annotation(g: Graph, args: Tuple[Any, ...], unqualified_op_name: str): ... def _jit_can_fuse_on_cpu() -> _bool: ... def _jit_can_fuse_on_gpu() -> _bool: ... def _jit_can_fuse_on_cpu_legacy() -> _bool: ... def _debug_get_fusion_group_inlining() -> _bool: ... def _debug_set_fusion_group_inlining(enable: _bool): ... def _jit_texpr_fuser_enabled() -> _bool: ... def _jit_nvfuser_enabled() -> _bool: ... def _llvm_enabled() -> _bool: ... def _jit_override_can_fuse_on_cpu(override: _bool): ... def _jit_override_can_fuse_on_gpu(override: _bool): ... def _jit_override_can_fuse_on_cpu_legacy(override: _bool): ... def _jit_set_symbolic_shapes_test_mode(override: _bool): ... def _jit_symbolic_shapes_test_mode_enabled() -> _bool: ... def _jit_set_texpr_fuser_enabled(enable: _bool): ... def _jit_set_te_must_use_llvm_cpu(use_llvm: _bool): ... def _jit_set_nvfuser_enabled(enable: _bool) -> _bool: ... def _jit_cat_wo_conditionals(optimize_cat: _bool): ... def _jit_opt_conditionals(opt_conds: _bool): ... def _jit_pass_canonicalize(graph: Graph): ... def _jit_pass_erase_shape_information(graph: Graph): ... def _jit_pass_fold_convbn(module: 'torch.jit.ScriptModule'): ... def _jit_pass_insert_observers(module: 'torch.jit.ScriptModule', method_name: str, qconfig_dict: Dict[str, Any], inplace: _bool, quant_type: _int): ... def _jit_pass_insert_quant_dequant(module: 'torch.jit.ScriptModule', method_name: str, inplace: _bool, debug: _bool, quant_type: _int): ... def _jit_pass_quant_finalize(module: 'torch.jit.ScriptModule', quant_type: _int, preserved_attrs: Sequence[str]): ... def _jit_set_profiling_executor(profiling_flag: _bool) -> _bool: ... def _jit_set_profiling_mode(profiling_flag: _bool) -> _bool: ... def _jit_try_infer_type(obj: Any) -> InferredType: ... def _jit_get_trigger_value(trigger_name: str) -> _int: ... # Defined in torch/csrc/jit/python/script_init.cpp ResolutionCallback = Callable[[str], Callable[..., Any]] # Defined in torch/csrc/jit/python/script_init.cpp # and torch/csrc/jit/python/init.cpp def _create_function_from_graph(qualname: str, graph: Graph) -> Graph: ... def _debug_set_autodiff_subgraph_inlining(disabled: _bool) -> None: ... def _ivalue_tags_match(lhs: ScriptModule, rhs: ScriptModule) -> _bool: ... def _jit_assert_is_instance(obj: Any, type: JitType): ... def _jit_clear_class_registry() -> None: ... def _jit_set_emit_hooks(ModuleHook: Optional[Callable], FunctionHook: Optional[Callable]) -> None: ... def _jit_get_emit_hooks() -> Tuple[Callable, Callable]: ... def _load_for_lite_interpreter(filename: Union[str, Path], map_location: Union[_device, str, None]): ... def _load_for_lite_interpreter_from_buffer(buffer: BinaryIO, map_location: Union[_device, str, None]): ... def _export_operator_list(module: LiteScriptModule): ... def _get_model_bytecode_version(filename: Union[str, Path]) -> _int: ... def _get_model_bytecode_version_from_buffer(buffer: BinaryIO) -> _int: ... def _backport_for_mobile(filename_input: Union[str, Path], filename_output: Union[str, Path], to_version: _int) -> None: ... def _backport_for_mobile_from_buffer(buffer: BinaryIO, filename_output: Union[str, Path], to_version: _int) -> None: ... def _backport_for_mobile_to_buffer(filename_input: Union[str, Path], to_version: _int) -> bytes:... def _backport_for_mobile_from_buffer_to_buffer(buffer: BinaryIO, to_version: _int) -> bytes:... def _get_model_ops_and_info(filename: Union[str, Path]): ... def _get_model_ops_and_info_from_buffer(buffer: BinaryIO): ... def _logging_set_logger(logger: LoggerBase) -> LoggerBase: ... def _get_graph_executor_optimize() -> _bool: ... def _set_graph_executor_optimize(optimize: _bool): ... def _export_opnames(module: ScriptModule) -> List[str]: ... def _create_function_from_trace( qualname: str, func: Callable[..., Any], input_tuple: Tuple[Any, ...], var_lookup_fn: Callable[[Tensor], str], strict: _bool, force_outplace: _bool, argument_names: List[str] ) -> Tuple[Graph, Stack]: ... def _jit_is_script_object(obj: Any) -> _bool: ... def _last_executed_optimized_graph() -> Graph: ... def parse_type_comment(comment: str) -> Decl: ... def merge_type_from_type_comment(decl: Decl, type_annotation_decl: Decl, is_method: _bool) -> Decl: ... def parse_ir(input: str) -> Graph: ... def parse_schema(schema: str) -> FunctionSchema: ... def get_device(input: Tensor) -> _int: ... def _resolve_type_from_object(obj: Any, range: SourceRange, rcb: ResolutionCallback) -> JitType: ... def _create_module_with_type(ty: JitType) -> ScriptModule: ... def _create_object_with_type(ty: ClassType) -> ScriptObject: ... def _run_emit_module_hook(m: ScriptModule): ... def _replace_overloaded_method_decl(overload_decl: Decl, implementation_def: Def, new_name: str) -> Def: ... def _jit_pass_lower_all_tuples(graph: Graph) -> None: ... def _jit_pass_onnx_set_dynamic_input_shape(graph: Graph, dynamic_axes: Dict[str, Dict[_int, str]], input_names: List[str]) -> None: ... def _jit_pass_onnx_graph_shape_type_inference(graph: Graph, paramsDict: Dict[str, IValue], opset_version: _int) -> None: ... def _jit_pass_onnx_assign_output_shape(graph: Graph, tensors: List[Tensor], desc: IODescriptor, onnx_shape_inference: _bool = False) -> None: ... def _jit_pass_onnx_remove_inplace_ops_for_onnx(graph: Graph, module: Module) -> None: ... def _jit_pass_remove_inplace_ops(graph: Graph) -> None: ... def _jit_pass_canonicalize_graph_fuser_ops(graph: Graph) -> None: ... def _jit_pass_peephole(graph: Graph, addmm_fusion_enabled: _bool) -> None: ... def _jit_pass_fuse_addmm(graph: Graph) -> None: ... def _jit_pass_onnx_preprocess(graph: Graph) -> None: ... def _jit_pass_prepare_division_for_onnx(graph: Graph) -> None: ... def _jit_pass_onnx_remove_print(graph: Graph) -> None: ... def _jit_pass_onnx_preprocess_caffe2(graph: Graph) -> None: ... def _jit_pass_onnx_unpack_quantized_weights( graph: Graph, paramsDict: Dict[str, IValue] ) -> Dict[str, IValue]: ... def _jit_pass_onnx_quantization_insert_permutes( graph: Graph, paramsDict: Dict[str, IValue] ) -> Dict[str, IValue]: ... def _jit_pass_custom_pattern_based_rewrite_graph(pattern: str, fused_node_name: str, graph: Graph) -> None: ... def _jit_onnx_list_model_parameters(module: ScriptModule) -> Tuple[ScriptModule, List[IValue]]: ... def _jit_pass_erase_number_types(graph: Graph) -> None: ... def _jit_pass_onnx(graph: Graph, _jit_pass_onnx: _onnx.OperatorExportTypes) -> Graph: ... def _jit_pass_onnx_scalar_type_analysis(graph: Graph, lowprecision_cast: _bool, opset_version: _int) -> None: ... def _jit_pass_onnx_peephole(graph: Graph, opset_version: _int, fixed_batch_size: _bool) -> None: ... def _jit_pass_dce_allow_deleting_nodes_with_side_effects(graph: Graph) -> None: ... def _jit_pass_onnx_function_substitution(graph: Graph) -> None: ... def _jit_pass_onnx_fold_if(graph: Graph) -> None: ... def _jit_pass_lower_graph(graph: Graph, m: Module) -> Tuple[Graph, List[IValue]]: ... def _jit_pass_inline_fork_wait(graph: Graph) -> None: ... def _jit_pass_onnx_eval_peephole(graph: Graph, paramsDict: Dict[str, IValue]) -> Dict[str, IValue]: ... def _jit_pass_onnx_constant_fold(graph: Graph, paramsDict: Dict[str, IValue], opset_version: _int) -> Dict[str, IValue]: ... def _jit_pass_onnx_eliminate_unused_items(graph: Graph, paramsDict: Dict[str, IValue]) -> Dict[str, IValue]: ... def _jit_pass_onnx_cast_all_constant_to_floating(graph: Graph) -> None: ... def _jit_pass_filter_non_tensor_arguments(params: Dict[str, IValue]) -> Dict[str, Tensor]: ... def _jit_decay_packed_param_input_types(graph: Graph) -> None: ... def _jit_pass_onnx_node_shape_type_inference(n: Node, paramsDict: Dict[str, IValue], opset_version: _int) -> None: ... def _jit_onnx_convert_pattern_from_subblock(block: Block, n: Node, env: Dict[Value, Value]) -> List[Value]: ... def _jit_pass_onnx_block( old_block: Block, new_block: Block, operator_export_type: _onnx.OperatorExportTypes, env: Dict[Value, Value], is_sub_block: _bool ) -> Dict[Value, Value]: ... def _jit_pass_fixup_onnx_controlflow_node(n: Node, opset_version: _int) -> Node: ... def _jit_script_interface_compile(name: str, class_def: ClassDef, rcb: ResolutionCallback, is_module: _bool): ... def _jit_script_compile_overload( qualname: str, overload_decl: Decl, implementation_def: Def, rcb: ResolutionCallback, implementation_defaults: Dict[str, Any], signature: Any ): ... def _jit_script_compile( qual_name: str, definition: Def, rcb: ResolutionCallback, defaults: Dict[str, Any] ): ... def _jit_script_class_compile( qual_name: str, definition: ClassDef, defaults: Dict[str, Dict[str, Any]], rcb: ResolutionCallback ): ... def _parse_source_def(src: str) -> Def: ... def import_ir_module( cu: CompilationUnit, filename: Union[str, Path], map_location: Union[_device, str, None], extra_files: Dict[str, Any] ) -> ScriptModule: ... def import_ir_module_from_buffer( cu: CompilationUnit, buffer: BinaryIO, map_location: Union[_device, str, None], extra_files: Dict[str, Any] ) -> ScriptModule: ... def _import_ir_module_from_package( cu: CompilationUnit, reader: PyTorchFileReader, storage_context: DeserializationStorageContext, map_location: Union[_device, str, None], ts_id: str ) -> ScriptModule: ... def _assign_output_shapes(graph: Graph, inputs: List[Tensor]) -> Graph: ... def _check_onnx_proto(proto: str) -> None: ... def _propagate_and_assign_input_shapes( graph: Graph, inputs: Tuple[Tensor, ...], param_count_list: List[_int], with_grad: _bool, propagate: _bool ) -> Graph: ... # Defined in torch/csrc/jit/runtime/graph_executor.h class GraphExecutorState: ... # Defined in torch/torch/csrc/jit/ir/alias_analysis.h class AliasDb: def __str__(self) -> str: ... ... # Defined in torch/torch/csrc/jit/ir/ir.h class Graph: def eraseInput(self, i: _int) -> None: ... def alias_db(self) -> AliasDb: ... ... # Defined in torch/csrc/jit/ir/ir.h class Value: ... # Defined in torch/csrc/jit/ir/ir.h class Block: ... # Defined in torch/csrc/jit/ir/ir.h class Node: ... # Defined in torch/aten/src/ATen/core/function_schema.h class Argument: name: str type: JitType default_value: Optional[Any] def has_default_value(self) -> _bool: ... kwarg_only : _bool ... class FunctionSchema: arguments: List[Argument] returns: List[Argument] ... # Defined in torch/csrc/jit/python/script_init.cpp class ScriptModuleSerializer(object): def __init__(self, export_writer: PyTorchFileWriter) -> None: ... def serialize(self, model: ScriptModule, script_module_id: _int) -> None: ... def write_files(self) -> None: ... def storage_context(self) -> SerializationStorageContext: ... ... # Defined in torch/csrc/jit/python/script_init.cpp class SerializationStorageContext(object): def __init__(self) -> None: ... def has_storage(self, storage: Storage) -> _bool: ... def get_or_add_storage(self, storage: Storage) -> _int: ... ... # Defined in torch/csrc/jit/python/script_init.cpp class DeserializationStorageContext(object): def __init__(self) -> None: ... def get_storage(self, name: str, dtype: _dtype) -> Tensor: ... def has_storage(self, name: str) -> _bool: ... def add_storage(self, name: str, tensor: Tensor) -> _int: ... ... # Defined in torch/csrc/jit/python/script_init.cpp class ConcreteModuleTypeBuilder: def __init__(self, obj: Any) -> None: ... def set_module_dict(self): ... def set_module_list(self): ... def add_attribute(self, name: str, ty: JitType, is_param: _bool, is_buffer: _bool): ... def add_module(self, name: str, meta: ConcreteModuleType): ... def add_constant(self, name: str, value: Any): ... def add_overload(self, method_name: str, overloaded_method_names: List[str]): ... def add_builtin_function(self, name: str, symbol_name: str): ... def add_failed_attribute(self, name: str, failure_reason: str): ... def add_function_attribute(self, name: str, ty: JitType, func: Callable[..., Any]): ... def add_ignored_attribute(self, name: str): ... def add_ignored_attributes(self, names: List[str]): ... def add_forward_hook(self, hook: Callable[..., Any]): ... def add_forward_pre_hook(self, pre_hook: Callable[..., Any]): ... class ConcreteModuleType: def get_constants(self) -> Dict[str, Any]: ... def equals(self, other: 'ConcreteModuleType') -> _bool: ... @staticmethod def from_jit_type(ty: JitType) -> ConcreteModuleType: ... class CallStack: def __init__(self, name: str, range: SourceRange): ... class ErrorReport: def __init__(self, range: SourceRange) -> None: ... def what(self) -> str: ... @staticmethod def call_stack() -> str: ... class CompilationUnit: def __init__(self, lang: str=..., _frames_up: _int=...) -> None: ... def find_function(self, name: str) -> ScriptFunction: ... def __getattr__(self, name: str) -> ScriptFunction: ... def define(self, script: str, rcb: ResolutionCallback=..., _frames_up: _int=...): ... def get_interface(self, name: str) -> InterfaceType: ... def get_functions(self) -> List[ScriptFunction]: ... def create_function(self, name: str, graph: Graph, shouldMangle: _bool=...) -> ScriptFunction: ... def get_class(self, name: str) -> ClassType: ... class ScriptObject: def setattr(self, name: str, value: Any): ... class ScriptModule(ScriptObject): def _method_names(self) -> List[str]: ... def _get_method(self, name: str) -> ScriptMethod: ... class LiteScriptModule: def __call__(self, *input): ... def find_method(self, method_name: str): ... def forward(self, *input) -> List[str]: ... def run_method(self, method_name: str, *input): ... class ScriptFunction: def __call__(self, *args, **kwargs) -> Tensor: ... def save(self, filename: str, _extra_files: Dict[str, bytes]) -> None: ... def save_to_buffer(self, _extra_files: Dict[str, bytes]) -> bytes: ... @property def graph(self) -> Graph: ... def inlined_graph(self) -> Graph: ... def schema(self) -> FunctionSchema: ... def code(self) -> str: ... def name(self) -> str: ... @property def qualified_name(self) -> str: ... class ScriptMethod: graph: Graph @property def owner(self) -> ScriptModule: ... @property def name(self) -> str: ... class ModuleDict: def __init__(self, mod: ScriptModule) -> None: ... def items(self) -> List[Tuple[str, Any]]: ... class ParameterDict: def __init__(self, mod: ScriptModule) -> None: ... class BufferDict: def __init__(self, mod: ScriptModule) -> None: ... # Defined in torch/csrc/jit/api/module.h class Module: ... # Defined in torch/csrc/Module.cpp def _initExtension(shm_manager_path: str) -> None: ... # THPModule_initExtension def _autograd_init() -> _bool: ... # THPAutograd_initExtension def _add_docstr(obj: T, doc_obj: str) -> T: ... # THPModule_addDocStr def _init_names(arg: Sequence[Type]) -> None: ... # THPModule_initNames def _has_distributed() -> _bool: ... # THPModule_hasDistributed def _set_default_tensor_type(type) -> None: ... # THPModule_setDefaultTensorType def _set_default_dtype(d: _dtype) -> None: ... # THPModule_setDefaultDtype def _infer_size(arg1: Size, arg2: Size) -> Size: ... # THPModule_inferSize def _crash_if_csrc_asan() -> _int: ... # THPModule_crashIfCsrcASAN def _crash_if_csrc_ubsan() -> _int: ... # THPModule_crashIfCsrcUBSAN def _crash_if_aten_asan() -> _int: ... # THPModule_crashIfATenASAN def _show_config() -> str: ... # THPModule_showConfig def _cxx_flags() -> str: ... # THPModule_cxxFlags def _parallel_info() -> str: ... # THPModule_parallelInfo def _set_backcompat_broadcast_warn(arg: _bool) -> None: ... # THPModule_setBackcompatBroadcastWarn def _get_backcompat_broadcast_warn() -> _bool: ... # THPModule_getBackcompatBroadcastWarn def _set_backcompat_keepdim_warn(arg: _bool) -> None: ... # THPModule_setBackcompatKeepdimWarn def _get_backcompat_keepdim_warn() -> _bool: ... # THPModule_getBackcompatKeepdimWarn def get_num_thread() -> _int: ... # THPModule_getNumThreads def set_num_threads(nthreads: _int) -> None: ... # THPModule_setNumThreads def get_num_interop_threads() -> _int: ... # THPModule_getNumInteropThreads def set_num_interop_threads(nthreads: _int) -> None: ... # THPModule_setNumInteropThreads def _get_cudnn_enabled() -> _bool: ... # THPModule_userEnabledCuDNN def _set_cudnn_enabled(arg: _bool) -> None: ... # THPModule_setUserEnabledCuDNN def _get_mkldnn_enabled() -> _bool: ... # THPModule_userEnabledMkldnn def _set_mkldnn_enabled(arg: _bool) -> None: ... # THPModule_setUserEnabledMkldnn def _get_cudnn_benchmark() -> _bool: ... # THPModule_benchmarkCuDNN def _set_cudnn_benchmark(arg: _bool) -> None: ... # THPModule_setBenchmarkCuDNN def _get_cudnn_deterministic() -> _bool: ... # THPModule_deterministicCuDNN def _set_cudnn_deterministic(arg: _bool) -> None: ... # THPModule_setDeterministicCuDNN def _get_deterministic_algorithms() -> _bool: ... # THPModule_deterministicAlgorithms def _set_deterministic_algorithms(arg: _bool) -> None: ... # THPModule_setDeterministicAlgorithms def _get_warnAlways() -> _bool: ... # THPModule_warnAlways def _set_warnAlways(arg: _bool) -> None: ... # THPModule_setWarnAlways def _get_cudnn_allow_tf32() -> _bool: ... # THPModule_allowTF32CuDNN def _set_cudnn_allow_tf32(arg: _bool) -> None: ... # THPModule_setAllowTF32CuDNN def _get_cublas_allow_tf32() -> _bool: ... # THPModule_allowTF32CuBLAS def _set_cublas_allow_tf32(arg: _bool) -> None: ... # THPModule_setAllowTF32CuBLAS # NB: There is no Capsule type in typing, see # https://code.activestate.com/lists/python-dev/139675/ def _to_dlpack(data: Tensor) -> Any: ... # THPModule_toDLPack def _from_dlpack(data: Any) -> Tensor: ... # THPModule_fromDLPack def set_flush_denormal(arg: _bool) -> _bool: ... # THPModule_setFlushDenormal def get_default_dtype() -> _dtype: ... # THPModule_getDefaultDtype def _get_default_device() -> str: ... # THPModule_getDefaultDevice def _get_qengine() -> _int: ... # THPModule_qEngine def _set_qengine(qegine: _int) -> None: ... # THPModule_setQEngine def _supported_qengines() -> List[_int]: ... # THPModule_supportedQEngines def _is_xnnpack_enabled() -> _bool: ... # THPModule_isEnabledXNNPACK def _set_default_mobile_cpu_allocator() -> None: ... # THPModule_setDefaultMobileCPUAllocator def _unset_default_mobile_cpu_allocator() -> None: ... # THPModule_unsetDefaultMobileCPUAllocator def _is_torch_function_enabled() -> _bool: ... # THPModule_isEnabledTorchFunction def _has_torch_function(args: Iterable[Any]) -> _bool: ... # THPModule_has_torch_function def _has_torch_function_unary(Any) -> _bool: ... # THPModule_has_torch_function_unary def _has_torch_function_variadic(*args: Any) -> _bool: ... # THPModule_has_torch_function_variadic def _vmapmode_increment_nesting() -> _int: ... # THPModule_vmapmode_increment_nesting def _vmapmode_decrement_nesting() -> _int: ... # THPModule_vmapmode_decrement_nesting def _log_api_usage_once(str) -> None: ... # LogAPIUsageOnceFromPython def _demangle(str) -> str: ... # c10::demangle def _disabled_torch_function_impl(func: Callable, types: Iterable[Type], args: Tuple, kwargs: Dict) -> Any: ... # THPModule_disable_torch_function # Defined in `valgrind.h` and `callgrind.h` respecitively. def _valgrind_supported_platform() -> _bool: ... # NVALGRIND def _valgrind_toggle() -> None: ... # CALLGRIND_TOGGLE_COLLECT def _valgrind_toggle_and_dump_stats() -> None: ... # CALLGRIND_TOGGLE_COLLECT and CALLGRIND_DUMP_STATS has_openmp: _bool has_mkl: _bool has_lapack: _bool has_cuda: _bool has_mkldnn: _bool has_cudnn: _bool has_spectral: _bool _GLIBCXX_USE_CXX11_ABI: _bool default_generator: Generator # Defined in torch/csrc/autograd/init.cpp def _set_grad_enabled(enabled: _bool) -> None: ... def is_grad_enabled() -> _bool: ... def is_inference_mode_enabled() -> _bool: ... def set_autocast_enabled(enabled: _bool) -> None: ... def is_autocast_enabled() -> _bool: ... def clear_autocast_cache() -> None: ... def set_autocast_cpu_enabled(enabled: _bool) -> None: ... def is_autocast_cpu_enabled() -> _bool: ... def set_autocast_cpu_dtype(dtype: _dtype) -> None: ... def set_autocast_gpu_dtype(dtype: _dtype) -> None: ... def get_autocast_cpu_dtype() -> _dtype: ... def get_autocast_gpu_dtype() -> _dtype: ... def autocast_increment_nesting() -> _int: ... def autocast_decrement_nesting() -> _int: ... def is_autocast_cache_enabled() -> _bool: ... def set_autocast_cache_enabled(enabled: _bool) -> None: ... def set_anomaly_enabled(enabled: _bool) -> None: ... def is_anomaly_enabled() -> _bool: ... def _enter_dual_level() -> _int: ... def _exit_dual_level(level: _int) -> None: ... def _make_dual(tensor: Tensor, tangent: Tensor, level: _int) -> Tensor: ... def _unpack_dual(tensor: Tensor, level: _int) -> Tensor: ... def __set_forward_AD_enabled(enabled: _bool) -> None: ... def __is_forward_AD_enabled() -> _bool: ... def _register_default_hooks(pack_hook: Callable, unpack_hook: Callable) -> None: ... def _reset_default_hooks() -> None: ... def _enter_python_mode(cls: Type) -> None: ... def _exit_python_mode() -> None: ... class _InferenceMode(object): def __init__(self, mode: _bool) -> None: ... # Defined in torch/csrc/jit/python/script_init.cpp class LoggerBase(object): ... class NoopLogger(LoggerBase): ... class LockingLogger(LoggerBase): ... class AggregationType(Enum): SUM = 0 AVG = 1 class FileCheck(object): # TODO (add more FileCheck signature) def check_source_highlighted(self, highlight: str) -> 'FileCheck': ... def run(self, test_string: str) -> None: ... ... # Defined in torch/csrc/jit/python/init.cpp class PyTorchFileReader(object): @overload def __init__(self, name: str) -> None: ... @overload def __init__(self, buffer: BinaryIO) -> None: ... def get_record(self, name: str) -> bytes: ... ... class PyTorchFileWriter(object): @overload def __init__(self, name: str) -> None: ... @overload def __init__(self, buffer: BinaryIO) -> None: ... def write_record(self, name: str, data: bytes, size: _int) -> None: ... def write_end_of_file(self) -> None: ... def set_min_version(self, version: _int) -> None: ... def get_all_written_records(self) -> List[str]: ... def archive_name(self) -> str: ... ... def _jit_get_inline_everything_mode() -> _bool: ... def _jit_set_inline_everything_mode(enabled: _bool) -> None: ... def _jit_get_logging_option() -> str: ... def _jit_set_logging_option(option: str) -> None: ... def _jit_pass_dce(Graph) -> None: ... def _jit_pass_lint(Graph) -> None: ... # Defined in torch/csrc/jit/python/python_custome_class.cpp def _get_custom_class_python_wrapper(name: str, attr: str) -> Any: ... # Defined in torch/csrc/Generator.cpp class Generator(object): device: _device def __init__(self, device: Union[_device, str, None] = None) -> None: ... def get_state(self) -> Tensor: ... def set_state(self, _new_state: Tensor) -> Generator: ... def manual_seed(self, seed: _int) -> Generator: ... def seed(self) -> _int: ... def initial_seed(self) -> _int: ... # Defined in torch/csrc/utils/init.cpp class BenchmarkConfig(object): num_calling_threads: _int num_worker_threads: _int num_warmup_iters: _int num_iters: _int profiler_output_path: str class BenchmarkExecutionStats(object): latency_avg_ms: _float num_iters: _int class ThroughputBenchmark(object): def __init__(self, module: Any) -> None: ... def add_input(self, *args: Any, **kwargs: Any) -> None: ... def run_once(self, *args: Any, **kwargs: Any) -> Any: ... def benchmark(self, config: BenchmarkConfig) -> BenchmarkExecutionStats: ... # IDK if these are actually exposed here, hope they are namedtuple_det_lu_pivs = NamedTuple("namedtuple_det_lu_pivs", [("det", Tensor), ("lu", Tensor), ("pivs", Tensor)]) namedtuple_output_mask = NamedTuple("namedtuple_output_mask", [("output", Tensor), ("mask", Tensor)]) namedtuple_LU_pivots_info = NamedTuple("namedtuple_LU_pivots_info", [("LU", Tensor), ("pivots", Tensor), ("info", Tensor)]) namedtuple_primal_tangent = NamedTuple("namedtuple_primal_tangent", [("primal", Tensor), ("tangent", Tensor)]) namedtuple_min_max = NamedTuple("namedtuple_min_max", [("min", Tensor), ("max", Tensor)]) namedtuple_values_indices = NamedTuple("namedtuple_values_indices", [("values", Tensor), ("indices", Tensor)]) namedtuple_eigenvalues_eigenvectors = NamedTuple("namedtuple_eigenvalues_eigenvectors", [("eigenvalues", Tensor), ("eigenvectors", Tensor)]) namedtuple_mantissa_exponent = NamedTuple("namedtuple_mantissa_exponent", [("mantissa", Tensor), ("exponent", Tensor)]) namedtuple_a_tau = NamedTuple("namedtuple_a_tau", [("a", Tensor), ("tau", Tensor)]) namedtuple_hist_bin_edges = NamedTuple("namedtuple_hist_bin_edges", [("hist", Tensor), ("bin_edges", Tensor)]) namedtuple_solution_QR = NamedTuple("namedtuple_solution_QR", [("solution", Tensor), ("QR", Tensor)]) namedtuple_P_L_U = NamedTuple("namedtuple_P_L_U", [("P", Tensor), ("L", Tensor), ("U", Tensor)]) namedtuple_Q_R = NamedTuple("namedtuple_Q_R", [("Q", Tensor), ("R", Tensor)]) namedtuple_sign_logabsdet = NamedTuple("namedtuple_sign_logabsdet", [("sign", Tensor), ("logabsdet", Tensor)]) namedtuple_solution_LU = NamedTuple("namedtuple_solution_LU", [("solution", Tensor), ("LU", Tensor)]) namedtuple_U_S_V = NamedTuple("namedtuple_U_S_V", [("U", Tensor), ("S", Tensor), ("V", Tensor)]) namedtuple_solution_cloned_coefficient = NamedTuple("namedtuple_solution_cloned_coefficient", [("solution", Tensor), ("cloned_coefficient", Tensor)]) # Defined in torch/csrc/generic/Storage.cpp class DoubleStorageBase(object): ... class FloatStorageBase(object): ... class LongStorageBase(object): ... class IntStorageBase(object): ... class ShortStorageBase(object): ... class CharStorageBase(object): ... class ByteStorageBase(object): ... class BoolStorageBase(object): ... class HalfStorageBase(object): ... class BFloat16StorageBase(object): ... class ComplexDoubleStorageBase(object): ... class ComplexFloatStorageBase(object): ... class QUInt8StorageBase(object): ... class QInt8StorageBase(object): ... class QInt32StorageBase(object): ... class QUInt4x2StorageBase(object): ... class CudaDoubleStorageBase(object): ... class CudaFloatStorageBase(object): ... class CudaLongStorageBase(object): ... class CudaIntStorageBase(object): ... class CudaShortStorageBase(object): ... class CudaCharStorageBase(object): ... class CudaByteStorageBase(object): ... class CudaBoolStorageBase(object): ... class CudaHalfStorageBase(object): ... class CudaBFloat16StorageBase(object): ... class CudaComplexDoubleStorageBase(object): ... class CudaComplexFloatStorageBase(object): ... class CudaQUInt8StorageBase(object): ... class CudaQInt8StorageBase(object): ... class CudaQInt32StorageBase(object): ... class CudaQUInt4x2StorageBase(object): ... # TODO: where class DoubleTensor(Tensor): ... class FloatTensor(Tensor): ... class LongTensor(Tensor): ... class IntTensor(Tensor): ... class ShortTensor(Tensor): ... class HalfTensor(Tensor): ... class CharTensor(Tensor): ... class ByteTensor(Tensor): ... class BoolTensor(Tensor): ... # Defined in torch/csrc/autograd/python_engine.cpp class _ImperativeEngine: ... # Defined in torch/csrc/autograd/python_variable.cpp class _TensorMeta(type): pass # Defined in torch/csrc/autograd/python_variable.cpp class _TensorBase(metaclass=_TensorMeta): requires_grad: _bool shape: Size data: Tensor names: List[str] device: _device dtype: _dtype layout: _layout real: Tensor imag: Tensor T: Tensor ndim: _int output_nr: _int _version: _int _base: Optional[Tensor] _cdata: _int grad_fn: Any _grad_fn: Any _grad: Optional[Tensor] _backward_hooks: Optional[Dict[_int, Callable[[Tensor], Optional[Tensor]]]] def __abs__(self) -> Tensor: ... def __add__(self, other: Any) -> Tensor: ... @overload def __and__(self, other: Tensor) -> Tensor: ... @overload def __and__(self, other: Number) -> Tensor: ... @overload def __and__(self, other: Any) -> Tensor: ... def __bool__(self) -> builtins.bool: ... def __complex__(self) -> builtins.complex: ... def __div__(self, other: Any) -> Tensor: ... def __eq__(self, other: Any) -> Tensor: ... # type: ignore[override] def __float__(self) -> builtins.float: ... def __floordiv__(self, other: Any) -> Tensor: ... def __ge__(self, other: Any) -> Tensor: ... def __getitem__(self, indices: Union[None, _int, slice, Tensor, List, Tuple]) -> Tensor: ... def __gt__(self, other: Any) -> Tensor: ... def __iadd__(self, other: Any) -> Tensor: ... @overload def __iand__(self, other: Tensor) -> Tensor: ... @overload def __iand__(self, other: Number) -> Tensor: ... @overload def __iand__(self, other: Any) -> Tensor: ... def __idiv__(self, other: Any) -> Tensor: ... def __ifloordiv__(self, other: Any) -> Tensor: ... @overload def __ilshift__(self, other: Tensor) -> Tensor: ... @overload def __ilshift__(self, other: Number) -> Tensor: ... @overload def __ilshift__(self, other: Any) -> Tensor: ... def __imod__(self, other: Any) -> Tensor: ... def __imul__(self, other: Any) -> Tensor: ... def __index__(self) -> builtins.int: ... @overload def __init__(self, *args: Any, device: Union[_device, str, None]=None) -> None: ... @overload def __init__(self, storage: Storage) -> None: ... @overload def __init__(self, other: Tensor) -> None: ... @overload def __init__(self, size: _size, *, device: Union[_device, str, None]=None) -> None: ... def __int__(self) -> builtins.int: ... def __invert__(self) -> Tensor: ... @overload def __ior__(self, other: Tensor) -> Tensor: ... @overload def __ior__(self, other: Number) -> Tensor: ... @overload def __ior__(self, other: Any) -> Tensor: ... @overload def __irshift__(self, other: Tensor) -> Tensor: ... @overload def __irshift__(self, other: Number) -> Tensor: ... @overload def __irshift__(self, other: Any) -> Tensor: ... def __isub__(self, other: Any) -> Tensor: ... @overload def __ixor__(self, other: Tensor) -> Tensor: ... @overload def __ixor__(self, other: Number) -> Tensor: ... @overload def __ixor__(self, other: Any) -> Tensor: ... def __le__(self, other: Any) -> Tensor: ... def __long__(self) -> builtins.int: ... @overload def __lshift__(self, other: Tensor) -> Tensor: ... @overload def __lshift__(self, other: Number) -> Tensor: ... @overload def __lshift__(self, other: Any) -> Tensor: ... def __lt__(self, other: Any) -> Tensor: ... def __matmul__(self, other: Any) -> Tensor: ... def __mod__(self, other: Any) -> Tensor: ... def __mul__(self, other: Any) -> Tensor: ... def __ne__(self, other: Any) -> Tensor: ... # type: ignore[override] def __neg__(self) -> Tensor: ... def __nonzero__(self) -> builtins.bool: ... @overload def __or__(self, other: Tensor) -> Tensor: ... @overload def __or__(self, other: Number) -> Tensor: ... @overload def __or__(self, other: Any) -> Tensor: ... def __pow__(self, other: Any) -> Tensor: ... def __radd__(self, other: Any) -> Tensor: ... def __rand__(self, other: Any) -> Tensor: ... def __rfloordiv__(self, other: Any) -> Tensor: ... def __rmul__(self, other: Any) -> Tensor: ... def __ror__(self, other: Any) -> Tensor: ... def __rpow__(self, other: Any) -> Tensor: ... @overload def __rshift__(self, other: Tensor) -> Tensor: ... @overload def __rshift__(self, other: Number) -> Tensor: ... @overload def __rshift__(self, other: Any) -> Tensor: ... def __rsub__(self, other: Any) -> Tensor: ... def __rtruediv__(self, other: Any) -> Tensor: ... def __rxor__(self, other: Any) -> Tensor: ... def __setitem__(self, indices: Union[None, _int, slice, Tensor, List, Tuple], val: Union[Tensor, Number]) -> None: ... def __sub__(self, other: Any) -> Tensor: ... def __truediv__(self, other: Any) -> Tensor: ... @overload def __xor__(self, other: Tensor) -> Tensor: ... @overload def __xor__(self, other: Number) -> Tensor: ... @overload def __xor__(self, other: Any) -> Tensor: ... def _coalesced_(self, coalesced: _bool) -> Tensor: ... def _conj(self) -> Tensor: ... def _conj_physical(self) -> Tensor: ... def _dimI(self) -> _int: ... def _dimV(self) -> _int: ... def _indices(self) -> Tensor: ... def _is_view(self) -> _bool: ... def _make_subclass(cls, data: Tensor, require_grad: _bool = False) -> Tensor: ... def _neg_view(self) -> Tensor: ... def _nnz(self) -> _int: ... def _values(self) -> Tensor: ... def abs(self) -> Tensor: ... def abs_(self) -> Tensor: ... def absolute(self) -> Tensor: ... def absolute_(self) -> Tensor: ... def acos(self) -> Tensor: ... def acos_(self) -> Tensor: ... def acosh(self) -> Tensor: ... def acosh_(self) -> Tensor: ... def add(self, other: Union[Tensor, Number], *, alpha: Optional[Number]=1, out: Optional[Tensor]=None) -> Tensor: ... def add_(self, other: Union[Tensor, Number], *, alpha: Optional[Number]=1) -> Tensor: ... def addbmm(self, batch1: Tensor, batch2: Tensor, *, beta: Number=1, alpha: Number=1) -> Tensor: ... def addbmm_(self, batch1: Tensor, batch2: Tensor, *, beta: Number=1, alpha: Number=1) -> Tensor: ... def addcdiv(self, tensor1: Tensor, tensor2: Tensor, *, value: Number=1) -> Tensor: ... def addcdiv_(self, tensor1: Tensor, tensor2: Tensor, *, value: Number=1) -> Tensor: ... def addcmul(self, tensor1: Tensor, tensor2: Tensor, *, value: Number=1) -> Tensor: ... def addcmul_(self, tensor1: Tensor, tensor2: Tensor, *, value: Number=1) -> Tensor: ... def addmm(self, mat1: Tensor, mat2: Tensor, *, beta: Number=1, alpha: Number=1) -> Tensor: ... def addmm_(self, mat1: Tensor, mat2: Tensor, *, beta: Number=1, alpha: Number=1) -> Tensor: ... def addmv(self, mat: Tensor, vec: Tensor, *, beta: Number=1, alpha: Number=1) -> Tensor: ... def addmv_(self, mat: Tensor, vec: Tensor, *, beta: Number=1, alpha: Number=1) -> Tensor: ... def addr(self, vec1: Tensor, vec2: Tensor, *, beta: Number=1, alpha: Number=1) -> Tensor: ... def addr_(self, vec1: Tensor, vec2: Tensor, *, beta: Number=1, alpha: Number=1) -> Tensor: ... def align_as(self, other: Tensor) -> Tensor: ... @overload def align_to(self, order: Sequence[Union[str, ellipsis, None]], ellipsis_idx: _int) -> Tensor: ... @overload def align_to(self, names: Sequence[Union[str, ellipsis, None]]) -> Tensor: ... @overload def all(self) -> Tensor: ... @overload def all(self, dim: _int, keepdim: _bool=False) -> Tensor: ... @overload def all(self, dim: Union[str, ellipsis, None], keepdim: _bool=False) -> Tensor: ... def allclose(self, other: Tensor, rtol: _float=1e-05, atol: _float=1e-08, equal_nan: _bool=False) -> _bool: ... def amax(self, dim: Union[_int, _size]=(), keepdim: _bool=False) -> Tensor: ... def amin(self, dim: Union[_int, _size]=(), keepdim: _bool=False) -> Tensor: ... def aminmax(self, *, dim: Optional[_int]=None, keepdim: _bool=False) -> namedtuple_min_max: ... def angle(self) -> Tensor: ... @overload def any(self) -> Tensor: ... @overload def any(self, dim: _int, keepdim: _bool=False) -> Tensor: ... @overload def any(self, dim: Union[str, ellipsis, None], keepdim: _bool=False) -> Tensor: ... def apply_(self, callable: Callable) -> Tensor: ... def arccos(self) -> Tensor: ... def arccos_(self) -> Tensor: ... def arccosh(self) -> Tensor: ... def arccosh_(self) -> Tensor: ... def arcsin(self) -> Tensor: ... def arcsin_(self) -> Tensor: ... def arcsinh(self) -> Tensor: ... def arcsinh_(self) -> Tensor: ... def arctan(self) -> Tensor: ... def arctan_(self) -> Tensor: ... def arctanh(self) -> Tensor: ... def arctanh_(self) -> Tensor: ... def argmax(self, dim: Optional[_int]=None, keepdim: _bool=False) -> Tensor: ... def argmin(self, dim: Optional[_int]=None, keepdim: _bool=False) -> Tensor: ... @overload def argsort(self, dim: _int=-1, descending: _bool=False) -> Tensor: ... @overload def argsort(self, dim: Union[str, ellipsis, None], descending: _bool=False) -> Tensor: ... def as_strided(self, size: _size, stride: _size, storage_offset: Optional[_int]=None) -> Tensor: ... def as_strided_(self, size: _size, stride: _size, storage_offset: Optional[_int]=None) -> Tensor: ... def as_subclass(self, cls: Tensor) -> Tensor: ... def asin(self) -> Tensor: ... def asin_(self) -> Tensor: ... def asinh(self) -> Tensor: ... def asinh_(self) -> Tensor: ... def atan(self) -> Tensor: ... def atan2(self, other: Tensor) -> Tensor: ... def atan2_(self, other: Tensor) -> Tensor: ... def atan_(self) -> Tensor: ... def atanh(self) -> Tensor: ... def atanh_(self) -> Tensor: ... def baddbmm(self, batch1: Tensor, batch2: Tensor, *, beta: Number=1, alpha: Number=1) -> Tensor: ... def baddbmm_(self, batch1: Tensor, batch2: Tensor, *, beta: Number=1, alpha: Number=1) -> Tensor: ... @overload def bernoulli(self, *, generator: Optional[Generator]=None) -> Tensor: ... @overload def bernoulli(self, p: _float, *, generator: Optional[Generator]=None) -> Tensor: ... @overload def bernoulli_(self, p: Tensor, *, generator: Optional[Generator]=None) -> Tensor: ... @overload def bernoulli_(self, p: _float=0.5, *, generator: Optional[Generator]=None) -> Tensor: ... def bfloat16(self) -> Tensor: ... def bincount(self, weights: Optional[Tensor]=None, minlength: _int=0) -> Tensor: ... @overload def bitwise_and(self, other: Tensor) -> Tensor: ... @overload def bitwise_and(self, other: Number) -> Tensor: ... @overload def bitwise_and_(self, other: Tensor) -> Tensor: ... @overload def bitwise_and_(self, other: Number) -> Tensor: ... @overload def bitwise_left_shift(self, other: Tensor) -> Tensor: ... @overload def bitwise_left_shift(self, other: Number) -> Tensor: ... @overload def bitwise_left_shift_(self, other: Tensor) -> Tensor: ... @overload def bitwise_left_shift_(self, other: Number) -> Tensor: ... def bitwise_not(self) -> Tensor: ... def bitwise_not_(self) -> Tensor: ... @overload def bitwise_or(self, other: Tensor) -> Tensor: ... @overload def bitwise_or(self, other: Number) -> Tensor: ... @overload def bitwise_or_(self, other: Tensor) -> Tensor: ... @overload def bitwise_or_(self, other: Number) -> Tensor: ... @overload def bitwise_right_shift(self, other: Tensor) -> Tensor: ... @overload def bitwise_right_shift(self, other: Number) -> Tensor: ... @overload def bitwise_right_shift_(self, other: Tensor) -> Tensor: ... @overload def bitwise_right_shift_(self, other: Number) -> Tensor: ... @overload def bitwise_xor(self, other: Tensor) -> Tensor: ... @overload def bitwise_xor(self, other: Number) -> Tensor: ... @overload def bitwise_xor_(self, other: Tensor) -> Tensor: ... @overload def bitwise_xor_(self, other: Number) -> Tensor: ... def bmm(self, mat2: Tensor) -> Tensor: ... def bool(self) -> Tensor: ... @overload def broadcast_to(self, size: _size) -> Tensor: ... @overload def broadcast_to(self, *size: _int) -> Tensor: ... def byte(self) -> Tensor: ... def cauchy_(self, median: _float=0, sigma: _float=1, *, generator: Optional[Generator]=None) -> Tensor: ... def ceil(self) -> Tensor: ... def ceil_(self) -> Tensor: ... def char(self) -> Tensor: ... def cholesky(self, upper: _bool=False) -> Tensor: ... def cholesky_inverse(self, upper: _bool=False) -> Tensor: ... def cholesky_solve(self, input2: Tensor, upper: _bool=False) -> Tensor: ... def chunk(self, chunks: _int, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... @overload def clamp(self, min: Optional[Number]=None, max: Optional[Number]=None) -> Tensor: ... @overload def clamp(self, min: Optional[Tensor]=None, max: Optional[Tensor]=None) -> Tensor: ... @overload def clamp_(self, min: Optional[Number]=None, max: Optional[Number]=None) -> Tensor: ... @overload def clamp_(self, min: Optional[Tensor]=None, max: Optional[Tensor]=None) -> Tensor: ... @overload def clamp_max(self, max: Tensor) -> Tensor: ... @overload def clamp_max(self, max: Number) -> Tensor: ... @overload def clamp_max_(self, max: Tensor) -> Tensor: ... @overload def clamp_max_(self, max: Number) -> Tensor: ... @overload def clamp_min(self, min: Tensor) -> Tensor: ... @overload def clamp_min(self, min: Number) -> Tensor: ... @overload def clamp_min_(self, min: Tensor) -> Tensor: ... @overload def clamp_min_(self, min: Number) -> Tensor: ... @overload def clip(self, min: Optional[Number]=None, max: Optional[Number]=None) -> Tensor: ... @overload def clip(self, min: Optional[Tensor]=None, max: Optional[Tensor]=None) -> Tensor: ... @overload def clip_(self, min: Optional[Number]=None, max: Optional[Number]=None) -> Tensor: ... @overload def clip_(self, min: Optional[Tensor]=None, max: Optional[Tensor]=None) -> Tensor: ... def clone(self, *, memory_format: Optional[memory_format]=None) -> Tensor: ... def coalesce(self) -> Tensor: ... def col_indices(self) -> Tensor: ... def conj(self) -> Tensor: ... def conj_physical(self) -> Tensor: ... def conj_physical_(self) -> Tensor: ... def contiguous(self, memory_format=torch.contiguous_format) -> Tensor: ... def copy_(self, src: Tensor, non_blocking: _bool=False) -> Tensor: ... @overload def copysign(self, other: Tensor) -> Tensor: ... @overload def copysign(self, other: Number) -> Tensor: ... @overload def copysign_(self, other: Tensor) -> Tensor: ... @overload def copysign_(self, other: Number) -> Tensor: ... def corrcoef(self) -> Tensor: ... def cos(self) -> Tensor: ... def cos_(self) -> Tensor: ... def cosh(self) -> Tensor: ... def cosh_(self) -> Tensor: ... @overload def count_nonzero(self, dim: Optional[_int]=None) -> Tensor: ... @overload def count_nonzero(self, dim: _size) -> Tensor: ... @overload def count_nonzero(self, *dim: _int) -> Tensor: ... def cov(self, *, correction: _int=1, fweights: Optional[Tensor]=None, aweights: Optional[Tensor]=None) -> Tensor: ... def cpu(self) -> Tensor: ... def cross(self, other: Tensor, dim: Optional[_int]=None) -> Tensor: ... def crow_indices(self) -> Tensor: ... def cuda(self, device: Optional[Union[_device, _int, str]]=None, non_blocking: _bool=False) -> Tensor: ... @overload def cummax(self, dim: _int) -> namedtuple_values_indices: ... @overload def cummax(self, dim: Union[str, ellipsis, None]) -> namedtuple_values_indices: ... @overload def cummin(self, dim: _int) -> namedtuple_values_indices: ... @overload def cummin(self, dim: Union[str, ellipsis, None]) -> namedtuple_values_indices: ... @overload def cumprod(self, dim: _int, *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def cumprod(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def cumprod_(self, dim: _int, *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def cumprod_(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def cumsum(self, dim: _int, *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def cumsum(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def cumsum_(self, dim: _int, *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def cumsum_(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype]=None) -> Tensor: ... def data_ptr(self) -> _int: ... def deg2rad(self) -> Tensor: ... def deg2rad_(self) -> Tensor: ... def dense_dim(self) -> _int: ... def dequantize(self) -> Tensor: ... def det(self) -> Tensor: ... def detach(self) -> Tensor: ... def detach_(self) -> Tensor: ... def diag(self, diagonal: _int=0) -> Tensor: ... def diag_embed(self, offset: _int=0, dim1: _int=-2, dim2: _int=-1) -> Tensor: ... def diagflat(self, offset: _int=0) -> Tensor: ... @overload def diagonal(self, *, outdim: Union[str, ellipsis, None], dim1: Union[str, ellipsis, None], dim2: Union[str, ellipsis, None], offset: _int=0) -> Tensor: ... @overload def diagonal(self, offset: _int=0, dim1: _int=0, dim2: _int=1) -> Tensor: ... def diff(self, n: _int=1, dim: _int=-1, prepend: Optional[Tensor]=None, append: Optional[Tensor]=None) -> Tensor: ... def digamma(self) -> Tensor: ... def digamma_(self) -> Tensor: ... def dim(self) -> _int: ... def dist(self, other: Tensor, p: Number=2) -> Tensor: ... def div(self, other: Union[Tensor, Number], *, rounding_mode: Optional[str] = None) -> Tensor: ... def div_(self, other: Union[Tensor, Number], *, rounding_mode: Optional[str] = None) -> Tensor: ... @overload def divide(self, other: Tensor) -> Tensor: ... @overload def divide(self, other: Tensor, *, rounding_mode: Optional[str]) -> Tensor: ... @overload def divide(self, other: Number, *, rounding_mode: Optional[str]) -> Tensor: ... @overload def divide(self, other: Number) -> Tensor: ... @overload def divide_(self, other: Tensor) -> Tensor: ... @overload def divide_(self, other: Tensor, *, rounding_mode: Optional[str]) -> Tensor: ... @overload def divide_(self, other: Number, *, rounding_mode: Optional[str]) -> Tensor: ... @overload def divide_(self, other: Number) -> Tensor: ... def dot(self, tensor: Tensor) -> Tensor: ... def double(self) -> Tensor: ... @overload def dsplit(self, sections: _int) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... @overload def dsplit(self, indices: _size) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... @overload def dsplit(self, *indices: _int) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... def eig(self, eigenvectors: _bool=False) -> namedtuple_eigenvalues_eigenvectors: ... def element_size(self) -> _int: ... @overload def eq(self, other: Tensor) -> Tensor: ... @overload def eq(self, other: Number) -> Tensor: ... @overload def eq_(self, other: Tensor) -> Tensor: ... @overload def eq_(self, other: Number) -> Tensor: ... def equal(self, other: Tensor) -> _bool: ... def erf(self) -> Tensor: ... def erf_(self) -> Tensor: ... def erfc(self) -> Tensor: ... def erfc_(self) -> Tensor: ... def erfinv(self) -> Tensor: ... def erfinv_(self) -> Tensor: ... def exp(self) -> Tensor: ... def exp2(self) -> Tensor: ... def exp2_(self) -> Tensor: ... def exp_(self) -> Tensor: ... @overload def expand(self, size: _size, *, implicit: _bool=False) -> Tensor: ... @overload def expand(self, *size: _int, implicit: _bool=False) -> Tensor: ... def expand_as(self, other: Tensor) -> Tensor: ... def expm1(self) -> Tensor: ... def expm1_(self) -> Tensor: ... def exponential_(self, lambd: _float=1, *, generator: Optional[Generator]=None) -> Tensor: ... @overload def fill_(self, value: Tensor) -> Tensor: ... @overload def fill_(self, value: Number) -> Tensor: ... def fill_diagonal_(self, fill_value: Number, wrap: _bool=False) -> Tensor: ... def fix(self) -> Tensor: ... def fix_(self) -> Tensor: ... @overload def flatten(self, start_dim: _int=0, end_dim: _int=-1) -> Tensor: ... @overload def flatten(self, start_dim: _int, end_dim: _int, out_dim: Union[str, ellipsis, None]) -> Tensor: ... @overload def flatten(self, start_dim: Union[str, ellipsis, None], end_dim: Union[str, ellipsis, None], out_dim: Union[str, ellipsis, None]) -> Tensor: ... @overload def flatten(self, dims: Sequence[Union[str, ellipsis, None]], out_dim: Union[str, ellipsis, None]) -> Tensor: ... @overload def flip(self, dims: _size) -> Tensor: ... @overload def flip(self, *dims: _int) -> Tensor: ... def fliplr(self) -> Tensor: ... def flipud(self) -> Tensor: ... def float(self) -> Tensor: ... @overload def float_power(self, exponent: Tensor) -> Tensor: ... @overload def float_power(self, exponent: Number) -> Tensor: ... @overload def float_power_(self, exponent: Tensor) -> Tensor: ... @overload def float_power_(self, exponent: Number) -> Tensor: ... def floor(self) -> Tensor: ... def floor_(self) -> Tensor: ... def floor_divide(self, other: Union[Tensor, Number], *, out: Optional[Tensor]=None) -> Tensor: ... def floor_divide_(self, other: Union[Tensor, Number]) -> Tensor: ... def fmax(self, other: Tensor) -> Tensor: ... def fmin(self, other: Tensor) -> Tensor: ... @overload def fmod(self, other: Tensor) -> Tensor: ... @overload def fmod(self, other: Number) -> Tensor: ... @overload def fmod_(self, other: Tensor) -> Tensor: ... @overload def fmod_(self, other: Number) -> Tensor: ... def frac(self) -> Tensor: ... def frac_(self) -> Tensor: ... def frexp(self) -> namedtuple_mantissa_exponent: ... @overload def gather(self, dim: _int, index: Tensor, *, sparse_grad: _bool=False) -> Tensor: ... @overload def gather(self, dim: Union[str, ellipsis, None], index: Tensor, *, sparse_grad: _bool=False) -> Tensor: ... def gcd(self, other: Tensor) -> Tensor: ... def gcd_(self, other: Tensor) -> Tensor: ... @overload def ge(self, other: Tensor) -> Tensor: ... @overload def ge(self, other: Number) -> Tensor: ... @overload def ge_(self, other: Tensor) -> Tensor: ... @overload def ge_(self, other: Number) -> Tensor: ... def geometric_(self, p: _float, *, generator: Optional[Generator]=None) -> Tensor: ... def geqrf(self) -> namedtuple_a_tau: ... def ger(self, vec2: Tensor) -> Tensor: ... def get_device(self) -> _int: ... @overload def greater(self, other: Tensor) -> Tensor: ... @overload def greater(self, other: Number) -> Tensor: ... @overload def greater_(self, other: Tensor) -> Tensor: ... @overload def greater_(self, other: Number) -> Tensor: ... @overload def greater_equal(self, other: Tensor) -> Tensor: ... @overload def greater_equal(self, other: Number) -> Tensor: ... @overload def greater_equal_(self, other: Tensor) -> Tensor: ... @overload def greater_equal_(self, other: Number) -> Tensor: ... @overload def gt(self, other: Tensor) -> Tensor: ... @overload def gt(self, other: Number) -> Tensor: ... @overload def gt_(self, other: Tensor) -> Tensor: ... @overload def gt_(self, other: Number) -> Tensor: ... def half(self) -> Tensor: ... def hardshrink(self, lambd: Number=0.5) -> Tensor: ... def has_names(self) -> _bool: ... def heaviside(self, values: Tensor) -> Tensor: ... def heaviside_(self, values: Tensor) -> Tensor: ... def histc(self, bins: _int=100, min: Number=0, max: Number=0) -> Tensor: ... @overload def histogram(self, bins: Tensor, *, weight: Optional[Tensor]=None, density: _bool=False) -> namedtuple_hist_bin_edges: ... @overload def histogram(self, bins: _int=100, *, range: Optional[Sequence[_float]]=None, weight: Optional[Tensor]=None, density: _bool=False) -> namedtuple_hist_bin_edges: ... @overload def hsplit(self, sections: _int) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... @overload def hsplit(self, indices: _size) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... @overload def hsplit(self, *indices: _int) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... def hypot(self, other: Tensor) -> Tensor: ... def hypot_(self, other: Tensor) -> Tensor: ... def i0(self) -> Tensor: ... def i0_(self) -> Tensor: ... def igamma(self, other: Tensor) -> Tensor: ... def igamma_(self, other: Tensor) -> Tensor: ... def igammac(self, other: Tensor) -> Tensor: ... def igammac_(self, other: Tensor) -> Tensor: ... @overload def index_add(self, dim: _int, index: Tensor, source: Tensor) -> Tensor: ... @overload def index_add(self, dim: _int, index: Tensor, source: Tensor, *, alpha: Number) -> Tensor: ... @overload def index_add(self, dim: Union[str, ellipsis, None], index: Tensor, source: Tensor, *, alpha: Number=1) -> Tensor: ... @overload def index_add_(self, dim: _int, index: Tensor, source: Tensor) -> Tensor: ... @overload def index_add_(self, dim: _int, index: Tensor, source: Tensor, *, alpha: Number) -> Tensor: ... @overload def index_copy(self, dim: _int, index: Tensor, source: Tensor) -> Tensor: ... @overload def index_copy(self, dim: Union[str, ellipsis, None], index: Tensor, source: Tensor) -> Tensor: ... @overload def index_copy_(self, dim: _int, index: Tensor, source: Tensor) -> Tensor: ... @overload def index_copy_(self, dim: Union[str, ellipsis, None], index: Tensor, source: Tensor) -> Tensor: ... @overload def index_fill(self, dim: _int, index: Tensor, value: Tensor) -> Tensor: ... @overload def index_fill(self, dim: Union[str, ellipsis, None], index: Tensor, value: Tensor) -> Tensor: ... @overload def index_fill(self, dim: _int, index: Tensor, value: Number) -> Tensor: ... @overload def index_fill(self, dim: Union[str, ellipsis, None], index: Tensor, value: Number) -> Tensor: ... @overload def index_fill_(self, dim: _int, index: Tensor, value: Tensor) -> Tensor: ... @overload def index_fill_(self, dim: Union[str, ellipsis, None], index: Tensor, value: Tensor) -> Tensor: ... @overload def index_fill_(self, dim: _int, index: Tensor, value: Number) -> Tensor: ... @overload def index_fill_(self, dim: Union[str, ellipsis, None], index: Tensor, value: Number) -> Tensor: ... def index_put(self, indices: Optional[Union[Tuple[Tensor, ...], List[Tensor]]], values: Tensor, accumulate: _bool=False) -> Tensor: ... def index_put_(self, indices: Optional[Union[Tuple[Tensor, ...], List[Tensor]]], values: Tensor, accumulate: _bool=False) -> Tensor: ... @overload def index_select(self, dim: _int, index: Tensor) -> Tensor: ... @overload def index_select(self, dim: Union[str, ellipsis, None], index: Tensor) -> Tensor: ... def indices(self) -> Tensor: ... def inner(self, other: Tensor) -> Tensor: ... def int(self) -> Tensor: ... def int_repr(self) -> Tensor: ... def inverse(self) -> Tensor: ... def is_coalesced(self) -> _bool: ... def is_complex(self) -> _bool: ... def is_conj(self) -> _bool: ... def is_contiguous(self, memory_format=torch.contiguous_format) -> _bool: ... is_cuda: _bool def is_distributed(self) -> _bool: ... def is_floating_point(self) -> _bool: ... def is_inference(self) -> _bool: ... is_leaf: _bool is_meta: _bool is_mkldnn: _bool def is_neg(self) -> _bool: ... def is_nonzero(self) -> _bool: ... is_ort: _bool def is_pinned(self, device: Optional[Union[_device, str, None]]=None) -> _bool: ... is_quantized: _bool def is_same_size(self, other: Tensor) -> _bool: ... def is_set_to(self, tensor: Tensor) -> _bool: ... def is_signed(self) -> _bool: ... is_sparse: _bool is_sparse_csr: _bool is_vulkan: _bool def isclose(self, other: Tensor, rtol: _float=1e-05, atol: _float=1e-08, equal_nan: _bool=False) -> Tensor: ... def isfinite(self) -> Tensor: ... def isinf(self) -> Tensor: ... def isnan(self) -> Tensor: ... def isneginf(self) -> Tensor: ... def isposinf(self) -> Tensor: ... def isreal(self) -> Tensor: ... def item(self) -> Number: ... def kron(self, other: Tensor) -> Tensor: ... @overload def kthvalue(self, k: _int, dim: _int=-1, keepdim: _bool=False) -> namedtuple_values_indices: ... @overload def kthvalue(self, k: _int, dim: Union[str, ellipsis, None], keepdim: _bool=False) -> namedtuple_values_indices: ... def lcm(self, other: Tensor) -> Tensor: ... def lcm_(self, other: Tensor) -> Tensor: ... def ldexp(self, other: Tensor) -> Tensor: ... def ldexp_(self, other: Tensor) -> Tensor: ... @overload def le(self, other: Tensor) -> Tensor: ... @overload def le(self, other: Number) -> Tensor: ... @overload def le_(self, other: Tensor) -> Tensor: ... @overload def le_(self, other: Number) -> Tensor: ... @overload def lerp(self, end: Tensor, weight: Tensor) -> Tensor: ... @overload def lerp(self, end: Tensor, weight: Number) -> Tensor: ... @overload def lerp_(self, end: Tensor, weight: Tensor) -> Tensor: ... @overload def lerp_(self, end: Tensor, weight: Number) -> Tensor: ... @overload def less(self, other: Tensor) -> Tensor: ... @overload def less(self, other: Number) -> Tensor: ... @overload def less_(self, other: Tensor) -> Tensor: ... @overload def less_(self, other: Number) -> Tensor: ... @overload def less_equal(self, other: Tensor) -> Tensor: ... @overload def less_equal(self, other: Number) -> Tensor: ... @overload def less_equal_(self, other: Tensor) -> Tensor: ... @overload def less_equal_(self, other: Number) -> Tensor: ... def lgamma(self) -> Tensor: ... def lgamma_(self) -> Tensor: ... def log(self) -> Tensor: ... def log10(self) -> Tensor: ... def log10_(self) -> Tensor: ... def log1p(self) -> Tensor: ... def log1p_(self) -> Tensor: ... def log2(self) -> Tensor: ... def log2_(self) -> Tensor: ... def log_(self) -> Tensor: ... def log_normal_(self, mean: _float=1, std: _float=2, *, generator: Optional[Generator]=None) -> Tensor: ... @overload def log_softmax(self, dim: _int, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def log_softmax(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype]=None) -> Tensor: ... def logaddexp(self, other: Tensor) -> Tensor: ... def logaddexp2(self, other: Tensor) -> Tensor: ... @overload def logcumsumexp(self, dim: _int) -> Tensor: ... @overload def logcumsumexp(self, dim: Union[str, ellipsis, None]) -> Tensor: ... def logdet(self) -> Tensor: ... def logical_and(self, other: Tensor) -> Tensor: ... def logical_and_(self, other: Tensor) -> Tensor: ... def logical_not(self) -> Tensor: ... def logical_not_(self) -> Tensor: ... def logical_or(self, other: Tensor) -> Tensor: ... def logical_or_(self, other: Tensor) -> Tensor: ... def logical_xor(self, other: Tensor) -> Tensor: ... def logical_xor_(self, other: Tensor) -> Tensor: ... def logit(self, eps: Optional[_float]=None) -> Tensor: ... def logit_(self, eps: Optional[_float]=None) -> Tensor: ... @overload def logsumexp(self, dim: Union[_int, _size], keepdim: _bool=False) -> Tensor: ... @overload def logsumexp(self, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool=False) -> Tensor: ... def long(self) -> Tensor: ... def lstsq(self, A: Tensor) -> namedtuple_solution_QR: ... @overload def lt(self, other: Tensor) -> Tensor: ... @overload def lt(self, other: Number) -> Tensor: ... @overload def lt_(self, other: Tensor) -> Tensor: ... @overload def lt_(self, other: Number) -> Tensor: ... def lu_solve(self, LU_data: Tensor, LU_pivots: Tensor) -> Tensor: ... def map2_(self, x: Tensor, y: Tensor, callable: Callable) -> Tensor: ... def map_(self, tensor: Tensor, callable: Callable) -> Tensor: ... @overload def masked_fill(self, mask: Tensor, value: Tensor) -> Tensor: ... @overload def masked_fill(self, mask: Tensor, value: Number) -> Tensor: ... @overload def masked_fill_(self, mask: Tensor, value: Tensor) -> Tensor: ... @overload def masked_fill_(self, mask: Tensor, value: Number) -> Tensor: ... def masked_scatter(self, mask: Tensor, source: Tensor) -> Tensor: ... def masked_scatter_(self, mask: Tensor, source: Tensor) -> Tensor: ... def masked_select(self, mask: Tensor) -> Tensor: ... def matmul(self, other: Tensor) -> Tensor: ... def matrix_exp(self) -> Tensor: ... def matrix_power(self, n: _int) -> Tensor: ... @overload def max(self) -> Tensor: ... @overload def max(self, other: Tensor) -> Tensor: ... @overload def max(self, dim: _int, keepdim: _bool=False) -> namedtuple_values_indices: ... @overload def max(self, dim: Union[str, ellipsis, None], keepdim: _bool=False) -> namedtuple_values_indices: ... def maximum(self, other: Tensor) -> Tensor: ... @overload def mean(self, *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def mean(self, dim: Union[_int, _size], keepdim: _bool=False, *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def mean(self, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool=False, *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def median(self) -> Tensor: ... @overload def median(self, dim: _int, keepdim: _bool=False) -> namedtuple_values_indices: ... @overload def median(self, dim: Union[str, ellipsis, None], keepdim: _bool=False) -> namedtuple_values_indices: ... @overload def min(self) -> Tensor: ... @overload def min(self, other: Tensor) -> Tensor: ... @overload def min(self, dim: _int, keepdim: _bool=False) -> namedtuple_values_indices: ... @overload def min(self, dim: Union[str, ellipsis, None], keepdim: _bool=False) -> namedtuple_values_indices: ... def minimum(self, other: Tensor) -> Tensor: ... def mm(self, mat2: Tensor) -> Tensor: ... @overload def mode(self, dim: _int=-1, keepdim: _bool=False) -> namedtuple_values_indices: ... @overload def mode(self, dim: Union[str, ellipsis, None], keepdim: _bool=False) -> namedtuple_values_indices: ... @overload def moveaxis(self, source: _int, destination: _int) -> Tensor: ... @overload def moveaxis(self, source: _size, destination: _size) -> Tensor: ... @overload def movedim(self, source: _int, destination: _int) -> Tensor: ... @overload def movedim(self, source: _size, destination: _size) -> Tensor: ... def msort(self) -> Tensor: ... def mul(self, other: Union[Tensor, Number], *, out: Optional[Tensor]=None) -> Tensor: ... def mul_(self, other: Union[Tensor, Number]) -> Tensor: ... def multinomial(self, num_samples: _int, replacement: _bool=False, *, generator: Optional[Generator]=None) -> Tensor: ... @overload def multiply(self, other: Tensor) -> Tensor: ... @overload def multiply(self, other: Number) -> Tensor: ... @overload def multiply_(self, other: Tensor) -> Tensor: ... @overload def multiply_(self, other: Number) -> Tensor: ... def mv(self, vec: Tensor) -> Tensor: ... def mvlgamma(self, p: _int) -> Tensor: ... def mvlgamma_(self, p: _int) -> Tensor: ... def nan_to_num(self, nan: Optional[_float]=None, posinf: Optional[_float]=None, neginf: Optional[_float]=None) -> Tensor: ... def nan_to_num_(self, nan: Optional[_float]=None, posinf: Optional[_float]=None, neginf: Optional[_float]=None) -> Tensor: ... def nanmean(self, dim: Union[_int, _size]=(), keepdim: _bool=False, *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def nanmedian(self) -> Tensor: ... @overload def nanmedian(self, dim: _int, keepdim: _bool=False) -> namedtuple_values_indices: ... @overload def nanmedian(self, dim: Union[str, ellipsis, None], keepdim: _bool=False) -> namedtuple_values_indices: ... @overload def nanquantile(self, q: Tensor, dim: Optional[_int], keepdim: _bool, *, interpolation: str) -> Tensor: ... @overload def nanquantile(self, q: Tensor, dim: Optional[_int]=None, keepdim: _bool=False) -> Tensor: ... @overload def nanquantile(self, q: _float, dim: Optional[_int], keepdim: _bool, *, interpolation: str) -> Tensor: ... @overload def nanquantile(self, q: _float, dim: Optional[_int]=None, keepdim: _bool=False) -> Tensor: ... @overload def nansum(self, *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def nansum(self, dim: Union[_int, _size], keepdim: _bool=False, *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def narrow(self, dim: _int, start: Tensor, length: _int) -> Tensor: ... @overload def narrow(self, dim: _int, start: _int, length: _int) -> Tensor: ... def narrow_copy(self, dim: _int, start: _int, length: _int) -> Tensor: ... def ndimension(self) -> _int: ... @overload def ne(self, other: Tensor) -> Tensor: ... @overload def ne(self, other: Number) -> Tensor: ... @overload def ne_(self, other: Tensor) -> Tensor: ... @overload def ne_(self, other: Number) -> Tensor: ... def neg(self) -> Tensor: ... def neg_(self) -> Tensor: ... def negative(self) -> Tensor: ... def negative_(self) -> Tensor: ... def nelement(self) -> _int: ... @overload def new(self, *args: Any, device: Union[_device, str, None]=None) ->Tensor: ... @overload def new(self, storage: Storage) -> Tensor: ... @overload def new(self, other: Tensor) -> Tensor: ... @overload def new(self, size: _size, *, device: Union[_device, str, None]=None) -> Tensor: ... @overload def new_empty(self, size: _size, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ... @overload def new_empty(self, *size: _int, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ... def new_empty_strided(self, size: _size, stride: _size, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ... def new_full(self, size: _size, fill_value: Number, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ... @overload def new_ones(self, size: _size, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ... @overload def new_ones(self, size: _size, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ... @overload def new_ones(self, *size: _int, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ... def new_tensor(self, data: Any, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ... @overload def new_zeros(self, size: _size, *, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ... @overload def new_zeros(self, *size: _int, dtype: _dtype=None, layout: Optional[_layout]=strided, device: Union[_device, str, None]=None, pin_memory: _bool=False, requires_grad: _bool=False) -> Tensor: ... def nextafter(self, other: Tensor) -> Tensor: ... def nextafter_(self, other: Tensor) -> Tensor: ... @overload def nonzero(self, *, as_tuple: Literal[False]=False) -> Tensor: ... @overload def nonzero(self, *, as_tuple: Literal[True]) -> Tuple[Tensor, ...]: ... def normal_(self, mean: _float=0, std: _float=1, *, generator: Optional[Generator]=None) -> Tensor: ... @overload def not_equal(self, other: Tensor) -> Tensor: ... @overload def not_equal(self, other: Number) -> Tensor: ... @overload def not_equal_(self, other: Tensor) -> Tensor: ... @overload def not_equal_(self, other: Number) -> Tensor: ... def numel(self) -> _int: ... def numpy(self) -> Any: ... def orgqr(self, input2: Tensor) -> Tensor: ... def ormqr(self, input2: Tensor, input3: Tensor, left: _bool=True, transpose: _bool=False) -> Tensor: ... def outer(self, vec2: Tensor) -> Tensor: ... @overload def permute(self, dims: _size) -> Tensor: ... @overload def permute(self, *dims: _int) -> Tensor: ... def pin_memory(self, device: Optional[Union[_device, str, None]]=None) -> Tensor: ... def pinverse(self, rcond: _float=1e-15) -> Tensor: ... def polygamma(self, n: _int) -> Tensor: ... def polygamma_(self, n: _int) -> Tensor: ... def positive(self) -> Tensor: ... @overload def pow(self, exponent: Tensor) -> Tensor: ... @overload def pow(self, exponent: Number) -> Tensor: ... @overload def pow_(self, exponent: Tensor) -> Tensor: ... @overload def pow_(self, exponent: Number) -> Tensor: ... def prelu(self, weight: Tensor) -> Tensor: ... @overload def prod(self, *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def prod(self, dim: _int, keepdim: _bool=False, *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def prod(self, dim: Union[str, ellipsis, None], keepdim: _bool=False, *, dtype: Optional[_dtype]=None) -> Tensor: ... def put(self, index: Tensor, source: Tensor, accumulate: _bool=False) -> Tensor: ... def put_(self, index: Tensor, source: Tensor, accumulate: _bool=False) -> Tensor: ... def q_per_channel_axis(self) -> _int: ... def q_per_channel_scales(self) -> Tensor: ... def q_per_channel_zero_points(self) -> Tensor: ... def q_scale(self) -> _float: ... def q_zero_point(self) -> _int: ... def qr(self, some: _bool=True) -> namedtuple_Q_R: ... def qscheme(self) -> _qscheme: ... @overload def quantile(self, q: Tensor, dim: Optional[_int], keepdim: _bool, *, interpolation: str) -> Tensor: ... @overload def quantile(self, q: Tensor, dim: Optional[_int]=None, keepdim: _bool=False) -> Tensor: ... @overload def quantile(self, q: _float, dim: Optional[_int], keepdim: _bool, *, interpolation: str) -> Tensor: ... @overload def quantile(self, q: _float, dim: Optional[_int]=None, keepdim: _bool=False) -> Tensor: ... def rad2deg(self) -> Tensor: ... def rad2deg_(self) -> Tensor: ... @overload def random_(self, *, generator: Optional[Generator]=None) -> Tensor: ... @overload def random_(self, from_: _int, to: Optional[_int], *, generator: Optional[Generator]=None) -> Tensor: ... @overload def random_(self, to: _int, *, generator: Optional[Generator]=None) -> Tensor: ... def ravel(self) -> Tensor: ... def reciprocal(self) -> Tensor: ... def reciprocal_(self) -> Tensor: ... def record_stream(self, s: Stream) -> None: ... def refine_names(self, names: Sequence[Union[str, ellipsis, None]]) -> Tensor: ... def relu(self) -> Tensor: ... def relu_(self) -> Tensor: ... @overload def remainder(self, other: Tensor) -> Tensor: ... @overload def remainder(self, other: Number) -> Tensor: ... @overload def remainder_(self, other: Tensor) -> Tensor: ... @overload def remainder_(self, other: Number) -> Tensor: ... def rename(self, names: Optional[Sequence[Union[str, ellipsis, None]]]) -> Tensor: ... def rename_(self, names: Optional[Sequence[Union[str, ellipsis, None]]]) -> Tensor: ... def renorm(self, p: Number, dim: _int, maxnorm: Number) -> Tensor: ... def renorm_(self, p: Number, dim: _int, maxnorm: Number) -> Tensor: ... @overload def repeat(self, repeats: _size) -> Tensor: ... @overload def repeat(self, *repeats: _int) -> Tensor: ... @overload def repeat_interleave(self, repeats: Tensor, dim: Optional[_int]=None, *, output_size: Optional[_int]=None) -> Tensor: ... @overload def repeat_interleave(self, repeats: _int, dim: Optional[_int]=None, *, output_size: Optional[_int]=None) -> Tensor: ... def requires_grad_(self, mode: _bool=True) -> Tensor: ... @overload def reshape(self, shape: _size) -> Tensor: ... @overload def reshape(self, *shape: _int) -> Tensor: ... def reshape_as(self, other: Tensor) -> Tensor: ... @overload def resize_(self, size: _size, *, memory_format: Optional[memory_format]=None) -> Tensor: ... @overload def resize_(self, *size: _int, memory_format: Optional[memory_format]=None) -> Tensor: ... def resize_as_(self, the_template: Tensor, *, memory_format: Optional[memory_format]=None) -> Tensor: ... def resolve_conj(self) -> Tensor: ... def resolve_neg(self) -> Tensor: ... def retain_grad(self) -> None: ... def roll(self, shifts: Union[_int, _size], dims: Union[_int, _size]=()) -> Tensor: ... def rot90(self, k: _int=1, dims: _size=(0,1)) -> Tensor: ... def round(self) -> Tensor: ... def round_(self) -> Tensor: ... def rsqrt(self) -> Tensor: ... def rsqrt_(self) -> Tensor: ... @overload def scatter(self, dim: _int, index: Tensor, src: Tensor) -> Tensor: ... @overload def scatter(self, dim: _int, index: Tensor, src: Tensor, *, reduce: str) -> Tensor: ... @overload def scatter(self, dim: _int, index: Tensor, value: Number, *, reduce: str) -> Tensor: ... @overload def scatter(self, dim: Union[str, ellipsis, None], index: Tensor, src: Tensor) -> Tensor: ... @overload def scatter(self, dim: _int, index: Tensor, value: Number) -> Tensor: ... @overload def scatter(self, dim: Union[str, ellipsis, None], index: Tensor, value: Number) -> Tensor: ... @overload def scatter_(self, dim: _int, index: Tensor, src: Tensor) -> Tensor: ... @overload def scatter_(self, dim: _int, index: Tensor, src: Tensor, *, reduce: str) -> Tensor: ... @overload def scatter_(self, dim: _int, index: Tensor, value: Number, *, reduce: str) -> Tensor: ... @overload def scatter_(self, dim: _int, index: Tensor, value: Number) -> Tensor: ... @overload def scatter_add(self, dim: _int, index: Tensor, src: Tensor) -> Tensor: ... @overload def scatter_add(self, dim: Union[str, ellipsis, None], index: Tensor, src: Tensor) -> Tensor: ... def scatter_add_(self, dim: _int, index: Tensor, src: Tensor) -> Tensor: ... @overload def select(self, dim: _int, index: _int) -> Tensor: ... @overload def select(self, dim: Union[str, ellipsis, None], index: _int) -> Tensor: ... @overload def set_(self, storage: Storage, offset: _int, size: _size, stride: _size) -> Tensor: ... @overload def set_(self, storage: Storage) -> Tensor: ... def sgn(self) -> Tensor: ... def sgn_(self) -> Tensor: ... def short(self) -> Tensor: ... def sigmoid(self) -> Tensor: ... def sigmoid_(self) -> Tensor: ... def sign(self) -> Tensor: ... def sign_(self) -> Tensor: ... def signbit(self) -> Tensor: ... def sin(self) -> Tensor: ... def sin_(self) -> Tensor: ... def sinc(self) -> Tensor: ... def sinc_(self) -> Tensor: ... def sinh(self) -> Tensor: ... def sinh_(self) -> Tensor: ... @overload def size(self) -> Size: ... @overload def size(self, dim: _int) -> _int: ... def slogdet(self) -> namedtuple_sign_logabsdet: ... def smm(self, mat2: Tensor) -> Tensor: ... @overload def softmax(self, dim: _int, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def softmax(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype]=None) -> Tensor: ... def solve(self, A: Tensor) -> namedtuple_solution_LU: ... @overload def sort(self, *, stable: Optional[_bool], dim: _int=-1, descending: _bool=False) -> namedtuple_values_indices: ... @overload def sort(self, dim: _int=-1, descending: _bool=False) -> namedtuple_values_indices: ... @overload def sort(self, *, stable: Optional[_bool], dim: Union[str, ellipsis, None], descending: _bool=False) -> namedtuple_values_indices: ... @overload def sort(self, dim: Union[str, ellipsis, None], descending: _bool=False) -> namedtuple_values_indices: ... def sparse_dim(self) -> _int: ... def sparse_mask(self, mask: Tensor) -> Tensor: ... def sparse_resize_(self, size: _size, sparse_dim: _int, dense_dim: _int) -> Tensor: ... def sparse_resize_and_clear_(self, size: _size, sparse_dim: _int, dense_dim: _int) -> Tensor: ... @overload def split(self, split_size: _int, dim: _int=0) -> Sequence[Tensor]: ... @overload def split(self, split_size: Tuple[_int, ...], dim: _int=0) -> Sequence[Tensor]: ... def split_with_sizes(self, split_sizes: _size, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... def sqrt(self) -> Tensor: ... def sqrt_(self) -> Tensor: ... def square(self) -> Tensor: ... def square_(self) -> Tensor: ... @overload def squeeze(self) -> Tensor: ... @overload def squeeze(self, dim: _int) -> Tensor: ... @overload def squeeze(self, dim: Union[str, ellipsis, None]) -> Tensor: ... @overload def squeeze_(self) -> Tensor: ... @overload def squeeze_(self, dim: _int) -> Tensor: ... @overload def squeeze_(self, dim: Union[str, ellipsis, None]) -> Tensor: ... def sspaddmm(self, mat1: Tensor, mat2: Tensor, *, beta: Number=1, alpha: Number=1) -> Tensor: ... @overload def std(self, dim: Union[_int, _size], unbiased: _bool=True, keepdim: _bool=False) -> Tensor: ... @overload def std(self, dim: Optional[Union[_int, _size]], *, correction: Optional[_int], keepdim: _bool=False) -> Tensor: ... @overload def std(self, unbiased: _bool=True) -> Tensor: ... @overload def std(self, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool=True, keepdim: _bool=False) -> Tensor: ... @overload def std(self, dim: Sequence[Union[str, ellipsis, None]], *, correction: Optional[_int], keepdim: _bool=False) -> Tensor: ... def storage(self) -> Storage: ... def storage_offset(self) -> _int: ... def storage_type(self) -> Storage: ... @overload def stride(self) -> Tuple[_int]: ... @overload def stride(self, _int) -> _int: ... def sub(self, other: Union[Tensor, Number], *, alpha: Optional[Number]=1, out: Optional[Tensor]=None) -> Tensor: ... def sub_(self, other: Union[Tensor, Number], *, alpha: Optional[Number]=1) -> Tensor: ... @overload def subtract(self, other: Tensor, *, alpha: Number=1) -> Tensor: ... @overload def subtract(self, other: Number, alpha: Number=1) -> Tensor: ... @overload def subtract_(self, other: Tensor, *, alpha: Number=1) -> Tensor: ... @overload def subtract_(self, other: Number, alpha: Number=1) -> Tensor: ... @overload def sum(self, *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def sum(self, dim: Union[_int, _size], keepdim: _bool=False, *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def sum(self, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool=False, *, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def sum_to_size(self, size: _size) -> Tensor: ... @overload def sum_to_size(self, *size: _int) -> Tensor: ... def svd(self, some: _bool=True, compute_uv: _bool=True) -> namedtuple_U_S_V: ... def swapaxes(self, axis0: _int, axis1: _int) -> Tensor: ... def swapaxes_(self, axis0: _int, axis1: _int) -> Tensor: ... def swapdims(self, dim0: _int, dim1: _int) -> Tensor: ... def swapdims_(self, dim0: _int, dim1: _int) -> Tensor: ... def symeig(self, eigenvectors: _bool=False, upper: _bool=True) -> namedtuple_eigenvalues_eigenvectors: ... def t(self) -> Tensor: ... def t_(self) -> Tensor: ... def take(self, index: Tensor) -> Tensor: ... def take_along_dim(self, indices: Tensor, dim: Optional[_int]=None) -> Tensor: ... def tan(self) -> Tensor: ... def tan_(self) -> Tensor: ... def tanh(self) -> Tensor: ... def tanh_(self) -> Tensor: ... @overload def tensor_split(self, tensor_indices_or_sections: Tensor, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... @overload def tensor_split(self, sections: _int, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... @overload def tensor_split(self, indices: _size, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... @overload def tile(self, dims: _size) -> Tensor: ... @overload def tile(self, *dims: _int) -> Tensor: ... @overload def to(self, dtype: _dtype, non_blocking: _bool=False, copy: _bool=False) -> Tensor: ... @overload def to(self, device: Optional[Union[_device, str]]=None, dtype: Optional[_dtype]=None, non_blocking: _bool=False, copy: _bool=False) -> Tensor: ... @overload def to(self, other: Tensor, non_blocking: _bool=False, copy: _bool=False) -> Tensor: ... def to_dense(self, dtype: Optional[_dtype]=None) -> Tensor: ... def to_mkldnn(self, dtype: Optional[_dtype]=None) -> Tensor: ... @overload def to_sparse(self) -> Tensor: ... @overload def to_sparse(self, sparse_dim: _int) -> Tensor: ... def tolist(self) -> List: ... def topk(self, k: _int, dim: _int=-1, largest: _bool=True, sorted: _bool=True) -> namedtuple_values_indices: ... def trace(self) -> Tensor: ... @overload def transpose(self, dim0: _int, dim1: _int) -> Tensor: ... @overload def transpose(self, dim0: Union[str, ellipsis, None], dim1: Union[str, ellipsis, None]) -> Tensor: ... def transpose_(self, dim0: _int, dim1: _int) -> Tensor: ... def triangular_solve(self, A: Tensor, upper: _bool=True, transpose: _bool=False, unitriangular: _bool=False) -> namedtuple_solution_cloned_coefficient: ... def tril(self, diagonal: _int=0) -> Tensor: ... def tril_(self, diagonal: _int=0) -> Tensor: ... def triu(self, diagonal: _int=0) -> Tensor: ... def triu_(self, diagonal: _int=0) -> Tensor: ... def true_divide(self, other: Union[Tensor, Number], *, out: Optional[Tensor]=None) -> Tensor: ... def true_divide_(self, other: Union[Tensor, Number]) -> Tensor: ... def trunc(self) -> Tensor: ... def trunc_(self) -> Tensor: ... @overload def type(self, dtype: None=None, non_blocking: _bool=False) -> str: ... @overload def type(self, dtype: Union[str, _dtype], non_blocking: _bool=False) -> Tensor: ... def type_as(self, other: Tensor) -> Tensor: ... @overload def unbind(self, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... @overload def unbind(self, dim: Union[str, ellipsis, None]) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... @overload def unflatten(self, dim: _int, sizes: _size, names: Optional[Sequence[Union[str, ellipsis, None]]]=None) -> Tensor: ... @overload def unflatten(self, dim: Union[str, ellipsis, None], sizes: _size, names: Sequence[Union[str, ellipsis, None]]) -> Tensor: ... def unfold(self, dimension: _int, size: _int, step: _int) -> Tensor: ... def uniform_(self, from_: _float=0, to: _float=1, *, generator: Optional[Generator]=None) -> Tensor: ... def unsafe_chunk(self, chunks: _int, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... def unsafe_split(self, split_size: _int, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... def unsafe_split_with_sizes(self, split_sizes: _size, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... def unsqueeze(self, dim: _int) -> Tensor: ... def unsqueeze_(self, dim: _int) -> Tensor: ... def values(self) -> Tensor: ... @overload def var(self, dim: Union[_int, _size], unbiased: _bool=True, keepdim: _bool=False) -> Tensor: ... @overload def var(self, dim: Optional[Union[_int, _size]], *, correction: Optional[_int], keepdim: _bool=False) -> Tensor: ... @overload def var(self, unbiased: _bool=True) -> Tensor: ... @overload def var(self, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool=True, keepdim: _bool=False) -> Tensor: ... @overload def var(self, dim: Sequence[Union[str, ellipsis, None]], *, correction: Optional[_int], keepdim: _bool=False) -> Tensor: ... def vdot(self, other: Tensor) -> Tensor: ... @overload def view(self, dtype: _dtype) -> Tensor: ... @overload def view(self, size: _size) -> Tensor: ... @overload def view(self, *size: _int) -> Tensor: ... def view_as(self, other: Tensor) -> Tensor: ... @overload def vsplit(self, sections: _int) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... @overload def vsplit(self, indices: _size) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... @overload def vsplit(self, *indices: _int) -> Union[Tuple[Tensor, ...], List[Tensor]]: ... def where(self, condition: Tensor, other: Tensor) -> Tensor: ... @overload def xlogy(self, other: Tensor) -> Tensor: ... @overload def xlogy(self, other: Number) -> Tensor: ... @overload def xlogy_(self, other: Tensor) -> Tensor: ... @overload def xlogy_(self, other: Number) -> Tensor: ... def zero_(self) -> Tensor: ... # Defined in torch/csrc/multiprocessing/init.cpp def _multiprocessing_init() -> None: ... # Defined in torch/csrc/cuda/Module.cpp def _cuda_getCurrentStream(device: _int) -> _int: ... def _cuda_getDefaultStream(device: _int) -> _int: ... def _cuda_getCurrentBlasHandle() -> _int: ... def _cuda_setDevice(device: _int) -> None: ... def _cuda_getDevice() -> _int: ... def _cuda_getDeviceCount() -> _int: ... def _cuda_set_sync_debug_mode(warn_level: Union[_int, str]) -> None: ... def _cuda_get_sync_debug_mode() -> _int: ... def _cuda_sleep(cycles: _int) -> None: ... def _cuda_synchronize() -> None: ... def _cuda_ipc_collect() -> None: ... def _cuda_getArchFlags() -> Optional[str]: ... def _cuda_init() -> None: ... def _cuda_setStream(cuda_stream: _int) -> None: ... def _cuda_getCompiledVersion() -> _int: ... def _cuda_cudaHostAllocator() -> _int: ... def _cuda_cudaCachingAllocator_raw_alloc(size: _int, cuda_stream: _int) -> _int: ... def _cuda_cudaCachingAllocator_raw_delete(ptr: _int) -> None: ... def _cuda_setMemoryFraction(fraction: _float, device: _int) -> None: ... def _cuda_emptyCache() -> None: ... def _cuda_memoryStats(device: _int) -> Dict[str, Any]: ... def _cuda_resetAccumulatedMemoryStats(device: _int) -> None: ... def _cuda_resetPeakMemoryStats(device: _int) -> None: ... def _cuda_memorySnapshot() -> List[Dict[str, Any]]: ... def _cuda_lock_mutex() -> None: ... def _cuda_unlock_mutex() -> None: ... def _cuda_canDeviceAccessPeer(device: _int, peer_device: _int) -> _bool: ... def _nccl_version() -> _int: ... def _nccl_unique_id() -> bytes: ... def _nccl_init_rank(nranks: _int, comm_id: bytes, rank: _int) -> object: ... def _nccl_reduce(input: Sequence[Tensor], output: Tensor, root: _int, op: _int, streams: Optional[Sequence[_CudaStreamBase]], comms: Optional[Sequence[object]]) -> None: ... def _nccl_all_reduce(input: Sequence[Tensor], output: Sequence[Tensor], op: _int, streams: Optional[Sequence[_CudaStreamBase]], comms: Optional[Sequence[object]]) -> None: ... def _nccl_broadcast(input: Sequence[Tensor], root: _int, streams: Optional[Sequence[_CudaStreamBase]], comms: Optional[Sequence[object]]) -> None: ... def _nccl_all_gather(input: Sequence[Tensor], output: Sequence[Tensor], streams: Optional[Sequence[_CudaStreamBase]], comms: Optional[Sequence[object]]) -> None: ... def _nccl_reduce_scatter(input: Sequence[Tensor], output: Sequence[Tensor], op: _int, streams: Optional[Sequence[_CudaStreamBase]], comms: Optional[Sequence[object]]) -> None: ... class _CudaDeviceProperties: name: str major: _int minor: _int multi_processor_count: _int total_memory: _int is_integrated: _int is_multi_gpu_board: _int # Defined in torch/csrc/cuda/python_comm.cpp def _broadcast(tensor: Tensor, devices: List[_int]) -> List[Tensor]: ... def _broadcast_out(tensor: Tensor, out_tensors: List[Tensor]) -> List[Tensor]: ... def _broadcast_coalesced( tensors: List[Tensor], devices: List[_int], buffer_size: _int ) -> List[List[Tensor]]: ... def _scatter(tensor: Tensor, devices: List[_int], chunk_sizes: Optional[List[_int]], dim: _int, streams: Optional[List[Stream]]) -> List[Tensor]: ... def _scatter_out(tensor: Tensor, out_tensors: List[Tensor], dim: _int, streams: Optional[List[Stream]]) -> List[Tensor]: ... def _gather(tensors: List[Tensor], dim: _int, destination_index: Optional[_int]) -> Tensor: ... def _gather_out(tensors: List[Tensor], out_tensor: Tensor, dim: _int) -> Tensor: ... # Defined in torch/csrc/cuda/Stream.cpp class _CudaStreamBase: _cdata: _int device: _device cuda_stream: _int priority: _int def __new__(self, priority: _int = 0, _cdata: _int = 0, stream_ptr: _int = 0) -> _CudaStreamBase: ... def query(self) -> _bool: ... def synchronize(self) -> None: ... def priority_range(self) -> Tuple[_int, _int]: ... # Defined in torch/csrc/cuda/Event.cpp class _CudaEventBase: device: _device cuda_event: _int def __new__(cls, enable_timing: _bool = False, blocking: _bool = False, interprocess: _bool = False) -> _CudaEventBase: ... @classmethod def from_ipc_handle(cls, device: _device, ipc_handle: bytes) -> _CudaEventBase: ... def record(self, stream: _CudaStreamBase) -> None: ... def wait(self, stream: _CudaStreamBase) -> None: ... def query(self) -> _bool: ... def elapsed_time(self, other: _CudaEventBase) -> _float: ... def synchronize(self) -> None: ... def ipc_handle(self) -> bytes: ... # Defined in torch/csrc/cuda/Graph.cpp class _CUDAGraph: def capture_begin(self, pool: Optional[Tuple[_int, _int]]=...) -> None: ... def capture_end(self) -> None: ... def replay(self) -> None: ... def reset(self) -> None: ... def pool(self) -> Tuple[_int, _int]: ... def _graph_pool_handle() -> Tuple[_int, _int]: ... # Defined in torch/csrc/DataLoader.cpp def _set_worker_signal_handlers(*arg: Any) -> None: ... # THPModule_setWorkerSignalHandlers def _set_worker_pids(key: _int, child_pids: Tuple[_int, ...]) -> None: ... # THPModule_setWorkerPIDs def _remove_worker_pids(loader_id: _int) -> None: ... # THPModule_removeWorkerPIDs def _error_if_any_worker_fails() -> None: ... # THPModule_errorIfAnyWorkerFails # Defined in torch/csrc/jit/python/python_tracer.cpp class TracingState: def push_scope(self, scope_name: str) -> None: ... def pop_scope(self) -> None: ... def current_scope(self) -> str: ... def set_graph(self, graph: Graph) -> None: ... def graph(self) -> Graph: ... ... def _create_graph_by_tracing( func: Callable[..., Any], inputs: Any, var_name_lookup_fn: Callable[[Tensor], str], strict: Any, force_outplace: Any, self: Any = None, argument_names: List[str] = [] ) -> Tuple[Graph, Stack]: ... def _tracer_warn_use_python(): ... def _get_tracing_state() -> TracingState: ... # Defined in torch/csrc/jit/python/python_ir.cpp # Not actually defined in python_ir.cpp, not sure where they are. class IValue: ... Stack = List[IValue] class JitType: annotation_str : str def isSubtypeOf(self, other: JitType) -> _bool: ... class InferredType: def __init__(self, arg: Union[JitType, str]): ... def type(self) -> JitType: ... def success(self) -> _bool: ... def reason(self) -> str: ... R = TypeVar('R', bound=JitType) class AnyType(JitType): @staticmethod def get() -> AnyType: ... class NoneType(JitType): @staticmethod def get() -> NoneType: ... class BoolType(JitType): @staticmethod def get() -> BoolType: ... class FloatType(JitType): @staticmethod def get() -> FloatType: ... class ComplexType(JitType): @staticmethod def get() -> ComplexType: ... class IntType(JitType): @staticmethod def get() -> IntType: ... class StringType(JitType): @staticmethod def get() -> StringType: ... class DeviceObjType(JitType): @staticmethod def get() -> DeviceObjType: ... class StreamObjType(JitType): @staticmethod def get() -> StreamObjType: ... class ListType(JitType): def __init__(self, a: JitType) -> None: ... def getElementType(self) -> JitType: ... @staticmethod def ofInts() -> ListType: ... @staticmethod def ofTensors() -> ListType: ... @staticmethod def ofFloats() -> ListType: ... @staticmethod def ofComplexDoubles() -> ListType: ... @staticmethod def ofBools() -> ListType: ... class DictType(JitType): def __init__(self, key: JitType, value: JitType) -> None: ... def getKeyType(self) -> JitType: ... def getValueType(self) -> JitType: ... class TupleType(JitType): def __init__(self, a: List[Optional[JitType]]) -> None: ... def elements(self) -> List[JitType]: ... class UnionType(JitType): def __init__(self, a: List[JitType]) -> None: ... class ClassType(JitType): def __init__(self, qualified_name: str) -> None: ... class InterfaceType(JitType): def __init__(self, qualified_name: str) -> None: ... def getMethod(self, name: str) -> Optional[FunctionSchema]: ... def getMethodNames(self) -> List[str]: ... class OptionalType(JitType, Generic[R]): def __init__(self, a: JitType) -> None: ... def getElementType(self) -> JitType: ... @staticmethod def ofTensor() -> OptionalType: ... class FutureType(JitType): def __init__(self, a: JitType) -> None: ... def getElementType(self) -> JitType: ... class RRefType(JitType): def __init__(self, a: JitType) -> None: ... class EnumType(JitType): def __init__( self, qualified_name: str, value_type: JitType, enum_names_values: List[Any] ) -> None: ... class TensorType(JitType): @classmethod def get(cls) -> TensorType: ... @classmethod def getInferred(cls) -> TensorType: ... def with_sizes(self, other: Optional[List[Optional[_int]]]) -> TensorType: ... def sizes(self) -> Optional[List[_int]]: ... # Defined in torch/csrc/jit/python/python_tree_views.cpp class SourceRange: ... class TreeView: ... class Ident(TreeView): @property def name(self) -> str: ... class ClassDef(TreeView): ... class Def(TreeView): def name(self) -> Ident: ... class Decl(TreeView): ... # Defined in torch/csrc/distributed/rpc/init.cpp def _rpc_init() -> _bool: ... # Defined in torch/csrc/distributed/autograd/init.cpp def _dist_autograd_init() -> _bool: ... # Defined in torch/csrc/distributed/c10d/init.cpp def _c10d_init() -> _bool: ... # Defined in torch/csrc/distributed/rpc/testing/init.cpp def _faulty_agent_init() -> _bool: ... def _enable_minidumps(directory: str) -> None: ... def _disable_minidumps() -> None: ... def _enable_minidumps_on_exceptions() -> None: ...