pytorch/torch/_C/__init__.pyi.in

# ${generated_comment}

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, SymInt, _dispatchkey
)
from torch.storage import TypedStorage

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
from . import _functorch as _functorch
from . import _lazy as _lazy
from . import _lazy_ts_backend as _lazy_ts_backend

T = TypeVar('T')
S = TypeVar("S", bound="torch.Tensor")

# 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  # type: ignore[override]
    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
    smallest_normal: _float
    resolution: _float
    dtype: str

    @overload
    def __init__(self, dtype: _dtype) -> None: ...

    @overload
    def __init__(self) -> None: ...

${dtype_class_hints}

# 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 = ...
sparse_csr : layout = ...
sparse_csc : layout = ...
sparse_bsr : layout = ...
sparse_bsc : 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/mobile_optimizer_type.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, optimize_numerics: _bool = True) -> None: ...
def _jit_pass_optimize_for_inference(module: 'torch.jit.ScriptModule',
                                     other_methods: List[str] = []) -> 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_concat_frozen_linear(graph: Graph): ...
def _jit_pass_convert_frozen_ops_to_mkldnn(graph: Graph): ...
def _jit_pass_transpose_frozen_linear(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) -> Tuple[Callable, List[str]]: ...
def _get_operation_overload(op_name: str, op_overload_name: str) -> Tuple[Callable, Callable, List[Any]]: ...
def _get_schema(op_name: str, overload_name: str) -> FunctionSchema: ...
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',
                                         optimization_blocklist: Set[MobileOptimizerType],
                                         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_register_decomposition_for_schema(schema: FunctionSchema, Graph) -> None: ...
def _jit_erase_non_input_shape_information(Graph) -> None: ...
def _jit_get_schemas_for_operator(name :str) -> List[FunctionSchema]: ...
def _jit_get_all_schemas() -> 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 _jit_llga_enabled() -> _bool: ...
def _jit_set_llga_enabled(enable: _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, keep_unique_names: _bool = True): ...
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_insert_quant_dequant_for_ondevice_ptq(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_pass_quant_finalize_for_ondevice_ptq(module: 'torch.jit.ScriptModule',
                             quant_type: _int,
                             method_name: str): ...
def _jit_pass_insert_observer_method_for_ondevice_ptq(module: 'torch.jit.ScriptModule',
                               method_name: str,
                               qconfig_dict: Dict[str, Any],
                               inplace: _bool,
                               quant_type: _int): ...
def _jit_set_profiling_executor(profiling_flag: _bool) -> _bool: ...
def _jit_set_profiling_mode(profiling_flag: _bool) -> _bool: ...
def _jit_set_fusion_strategy(strategy: List[Tuple[str, _int]]) -> List[Tuple[str, _int]]: ...
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) -> ScriptFunction: ...
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 _quantize_ondevice_ptq_dynamic(module: LiteScriptModule, method_name: str): ...
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 _get_mobile_model_contained_types(filename: Union[str, Path]): ...
def _get_mobile_model_contained_types_from_buffer(buffer: BinaryIO): ...
def _logging_set_logger(logger: LoggerBase) -> LoggerBase: ...
def _get_graph_executor_optimize(optimize: Optional[_bool] = None) -> _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 _create_function_from_trace_with_dict(
    qualname: str,
    func: Callable[..., Any],
    input_dict: Dict[str, 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 _get_upgraders_map_size() -> _int: ...
def _dump_upgraders_map() -> Dict[str, str]: ...
def _test_only_populate_upgraders(content: Dict[str, str]) -> None: ...
def _test_only_remove_upgraders(content: Dict[str, str]) -> None: ...
def merge_type_from_type_comment(decl: Decl, type_annotation_decl: Decl, is_method: _bool) -> Decl: ...
def parse_ir(input: str, parse_tensor_constants: _bool) -> 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, is_script: _bool) -> None: ...
def _jit_pass_onnx_remove_inplace_ops_for_onnx(graph: Graph, module: Optional[ScriptModule] = None) -> 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, disable_shape_peepholes: _bool = False) -> None: ...
def _jit_pass_onnx_autograd_function_process(graph: Graph) -> 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],
    caffe2: _bool
) -> 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_lint(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_function_extraction(graph: Graph, module_names : Set[str], param_names : List[str]) -> Dict[Node, Dict[str, str]]: ...
def _jit_pass_onnx_clear_scope_records() -> None: ...
def _jit_pass_onnx_track_scope_attributes(graph: Graph, onnx_attrs: Dict[str, Any]) -> None: ...
def _jit_is_onnx_log_enabled() -> _bool: ...
def _jit_set_onnx_log_enabled(enabled: _bool) -> None: ...
def _jit_set_onnx_log_output_stream(stream_name: str) -> None: ...
def _jit_onnx_log(*args: Any) -> 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_deduplicate_initializers(graph: Graph, params_dict: Dict[str, IValue], is_train: _bool) -> Dict[str, IValue]: ...
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_onnx_assign_scoped_names_for_node_and_value(graph: Graph) -> None: ...
def _jit_pass_fixup_onnx_controlflow_node(n: Node, opset_version: _int) -> List[Value]: ...
def _jit_onnx_create_full_scope_name(class_name: str, variable_name: str) -> str: ...

def _compile_graph_to_code_table(name: str, graph: Graph) -> IValue: ...

def _generate_upgraders_graph() -> Dict[str, Graph]: ...

def _calculate_package_version_based_on_upgraders(val: _bool): ...

def _get_version_calculator_flag() -> _bool: ...

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, full_check: _bool = False) -> 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: ...
    ...

class _InsertPoint:
    def __enter__(self) -> None: ...
    def __exit__(self, *args) -> None: ...

# Defined in torch/csrc/jit/ir/ir.h
class Use:
    @property
    def user(self) -> Node: ...
    @property
    def offset(self) -> _int: ...
    def isAfter(self, other: Use) -> _bool: ...
    ...

# Defined in torch/csrc/jit/ir/ir.h
class Value:
    def type(self)-> JitType: ...
    def setType(self, t: JitType) -> Value: ...
    def setTypeAs(self, other: Value) -> Value: ...
    def inferTypeFrom(self, t: Tensor) -> None: ...
    def debugName(self) -> str: ...
    def setDebugName(self, name: str) -> None: ...
    def unique(self) -> _int: ...
    def offset(self) -> _int: ...
    def node(self) -> Node: ...
    def uses(self) -> List[Use]: ...
    def replaceAllUsesWith(self, val: Value) -> None: ...
    def replaceAllUsesAfterNodeWith(self, node: Node, val: Value) -> None: ...
    def requires_grad(self) -> _bool: ...
    def requiresGrad(self) -> _bool: ...
    def copyMetadata(self, other: Value) -> Value: ...
    def isCompleteTensor(self) -> _bool: ...
    def toIValue(self) -> IValue: ...
    ...

# Defined in torch/csrc/jit/ir/ir.h
class Block:
    def inputs(self) -> Iterator[Value]: ...
    def outputs(self) -> Iterator[Value]: ...
    def nodes(self) -> Iterator[Node]: ...
    def paramNode(self) -> Node: ...
    def returnNode(self) -> Node: ...
    def owningNode(self) -> Node: ...
    def registerOutput(self, n: Value) -> _int: ...
    def addNode(self, name: str, inputs: Sequence[Value]) -> Node: ...
    ...

# Defined in torch/csrc/jit/ir/ir.h
class Node:
    def __getitem__(self, key: str) -> Any: ...
    def schema(self) -> str: ...
    def input(self) -> Value: ...
    def inputs(self) -> Iterator[Value]: ...
    def inputsAt(self, idx: _int) -> Value: ...
    def inputsSize(self) -> _int: ...
    def output(self) -> Value: ...
    def outputs(self) -> Iterator[Value]: ...
    def outputsAt(self, idx: _int) -> Value: ...
    def outputsSize(self) -> _int: ...
    def hasMultipleOutputs(self) -> _bool: ...
    def blocks(self) -> List[Block]: ...
    def addBlock(self) -> Block: ...
    def mustBeNone(self) -> _bool: ...
    def matches(self, pattern: str) -> _bool: ...
    def kind(self) -> str: ...
    def kindOf(self, name: str) -> str: ...
    def addInput(self, name: str) -> Value: ...
    def replaceInput(self, i: _int, newValue: Value) -> Value: ...
    def replaceInputWith(self, from_: Value, to: Value) -> None: ...
    def replaceAllUsesWith(self, n: Node) -> None: ...
    def insertBefore(self, n: Node) -> Node: ...
    def insertAfter(self, n: Node) -> Node: ...
    def isBefore(self, n: Node) -> _bool: ...
    def isAfter(self, n: Node) -> _bool: ...
    def moveBefore(self, n: Node) -> None: ...
    def moveAfter(self, n: Node) -> None: ...
    def removeInput(self, i: _int) -> None: ...
    def removeAllInputs(self, i: _int) -> None: ...
    def hasUses(self) -> _bool: ...
    def eraseOutput(self, i: _int) -> None: ...
    def addOutput(self) -> Value: ...
    def scopeName(self) -> str: ...
    def isNondeterministic(self) -> _bool: ...
    def copyAttributes(self, rhs: Node) -> Node: ...
    def copyMetadata(self, rhs: Node) -> Node: ...
    def hasAttributes(self) -> _bool: ...
    def hasAttribute(self, name: str) -> _bool: ...
    def removeAttribute(self, attr: str) -> Node: ...
    def namedInput(self, name: str) -> Value: ...
    def sourceRange(self) -> SourceRange: ...
    def owningBlock(self) -> Block: ...
    def findNode(self, kind: str, recurse: _bool = True) -> Node: ...
    def findAllNodes(self, kind: str, recurse: _bool = True) -> List[Node]: ...
    def getModuleHierarchy(self) -> str: ...
    def prev(self) -> Node: ...
    def destroy(self) -> None: ...
    def attributeNames(self) -> List[str]: ...

    # Accessors for attributes as types.
    def f(self, name: str) -> _float: ...
    def f_(self, name: str, val: _float) -> Node: ...
    def fs(self, name: str) -> List[_float]: ...
    def fs_(self, name: str, val: List[_float]) -> Node: ...
    def c(self, name: str) -> complex: ...
    def c_(self, name: str, val: complex) -> Node: ...
    def s(self, name: str) -> str: ...
    def s_(self, name: str, val: str) -> Node: ...
    def ss(self, name: str) -> List[str]: ...
    def ss_(self, name: str, val: List[str]) -> Node: ...
    def i(self, name: str) -> _int: ...
    def i_(self, name: str, val: _int) -> Node: ...
    # Cannot define "is" like this because it's a reserved keyword in python.
    # def is(self, name: str) -> List[_int]: ...
    # def is_(self, name: str, val: List[_int]) -> Node: ...
    def g(self, name: str) -> Graph: ...
    def g_(self, name: str, val: Graph) -> Node: ...
    def gs(self, name: str) -> List[Graph]: ...
    def gs_(self, name: str, val: List[Graph]) -> Node: ...
    def ival(self, name: str) -> IValue: ...
    def ival_(self, name: str, val: IValue) -> Node: ...
    def t(self, name: str) -> Tensor: ...
    def t_(self, name: str, val: Tensor) -> Node: ...
    def ts(self, name: str) -> List[Tensor]: ...
    def ts_(self, name: str, val: List[Tensor]) -> Node: ...
    def ty(self, name: str) -> JitType: ...
    def ty_(self, name: str, val: JitType) -> Node: ...
    def tys(self, name: str) -> List[JitType]: ...
    def tys_(self, name: str, val: List[JitType]) -> Node: ...
    ...

# Defined in torch/torch/csrc/jit/ir/ir.h
class Graph:
    def inputs(self) -> Iterator[Value]: ...
    def outputs(self) -> Iterator[Value]: ...
    def nodes(self) -> Iterator[Node]: ...
    def param_node(self) -> Node: ...
    def return_node(self) -> Node: ...
    def addInput(self, name: str) -> Value: ...
    def eraseInput(self, i: _int) -> None: ...
    def registerOutput(self, n: Value) -> _int: ...
    def eraseOutput(self, i: _int) -> None: ...
    def create(self, name: str, args, num_outputs: _int) -> Node: ...
    def appendNode(self, n: Node) -> Node: ...
    def prependNode(self, n: Node) -> Node: ...
    def insertNode(self, n: Node) -> Node: ...
    def block(self) -> Block: ...
    def lint(self) -> None: ...
    def alias_db(self) -> AliasDb: ...
    def setInsertPoint(self, n: Union[Block, Node]) -> None: ...
    def insert_point_guard(self, n: Union[Block, Node]) -> _InsertPoint: ...
    def insertPoint(self) -> Node: ...
    def insertGraph(self, callee: Graph, inputs: List[Value]) -> List[Value]: ...
    def makeMultiOutputIntoTuple(self) -> None: ...
    ...


# Defined in torch/aten/src/ATen/core/alias_info.h
class AliasInfo:
    is_write: _bool
    before_set: Set[str]
    after_set: Set[str]


# 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
    is_out: _bool
    alias_info: Optional[AliasInfo]
    ...
class FunctionSchema:
    arguments: List[Argument]
    returns: List[Argument]
    name: str
    overload_name: str
    ...

class _UpgraderEntry:
    bumped_at_version: _int
    upgrader_name: str
    old_schema: str
    def __init__(self, bumped_at_version: _int, upgrader_name: str, old_schema: str) -> None: ...

class _UpgraderRange:
    min_version: _int
    max_version: _int

def _get_max_operator_version() -> _int: ...

def _get_operator_version_map() -> Dict[str, List[_UpgraderEntry]]: ...

def _get_upgrader_ranges(name: str) -> List[_UpgraderRange]: ...

def _test_only_add_entry_to_op_version(op_name: str, entry: _UpgraderEntry) -> None: ...

def _test_only_remove_entry_to_op_version(op_name: str) -> None: ...

# 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 set_parameter_list(self): ...
    def set_parameter_dict(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_flash_sdp_enabled() -> _bool: ...  # THPModule_userEnabledFusedSDP
def _set_sdp_use_flash(arg: _bool) -> None: ...  # THPModule_setSDPUseFlash
def _get_mem_efficient_sdp_enabled() -> _bool: ...  # THPModule_userEnabledMathSDP
def _set_sdp_use_mem_efficient(arg: _bool) -> None: ...  # THPModule_setSDPUseMemEfficient
def _get_math_sdp_enabled() -> _bool: ...  # THPModule_userEnabledMathSDP
def _set_sdp_use_math(arg: _bool) -> None: ...  # THPModule_setSDPUseMath
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 _get_deterministic_algorithms_warn_only() -> _bool: ...  # THPModule_deterministicAlgorithmsWarnOnly
def _set_deterministic_algorithms(mode: _bool, *, warn_only: _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
def _get_float32_matmul_precision() -> str: ... #THPModule_float32MatmulPrecision
def _set_float32_matmul_precision(arg: str) -> None: ... #THPModule_setFloat32MatmulPrecision
def _get_cublas_allow_fp16_reduced_precision_reduction() -> _bool: ... #THPModule_allowFP16ReductionCuBLAS
def _set_cublas_allow_fp16_reduced_precision_reduction(arg: _bool) -> None: ... #THPModule_setAllowFP16ReductionCuBLAS
def _set_conj(x: Tensor, conj: _bool) -> None: ...
def _set_neg(x: Tensor, neg: _bool) -> None: ...
def _set_meta_in_tls_dispatch_include(meta_in_tls: _bool) -> None: ...
def _meta_in_tls_dispatch_include() -> _bool: ...
def _select_conv_backend(*args, **kwargs) -> ConvBackend: ...
def _conv_determine_backend_memory_format(input: Tensor, weight: Tensor, backend: ConvBackend) -> memory_format: ...
def _has_storage(x: Tensor) -> _bool: ...
def _should_allow_numbers_as_tensors(func_name: str) -> _bool: ...
# 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 _get_cpp_backtrace(frames_to_skip: _int, maximum_number_of_frames: _int) -> str: ...  # THPModule_getCppBacktrace
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
def _disabled_torch_dispatch_impl(func: Callable, types: Iterable[Type], args: Tuple, kwargs: Dict) -> Any: ...  # THPModule_disable_dispatch_function
def _get_linalg_preferred_backend() -> torch._C._LinalgBackend: ...
def _set_linalg_preferred_backend(arg: torch._C._LinalgBackend): ...
def _is_mps_available() -> _bool: ...
class _LinalgBackend:
    Default: _LinalgBackend
    Cusolver: _LinalgBackend
    Magma: _LinalgBackend

class ConvBackend(Enum):
    ...

# 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_mps: _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, check_nan: _bool = True) -> None: ...
def is_anomaly_enabled() -> _bool: ...
def is_anomaly_check_nan_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 _is_torch_function_mode_enabled()-> _bool: ...
def _set_torch_function_mode(cls: Any) -> None: ...
def _push_on_torch_function_stack(cls: Any) -> None: ...
def _pop_torch_function_stack() -> Any: ...
def _get_function_stack_at(idx: _int) -> Any: ...
def _len_torch_function_stack() -> _int: ...

def _set_torch_dispatch_mode(cls: Any) -> None: ...
def _push_on_torch_dispatch_stack(cls: Any) -> None: ...
def _pop_torch_dispatch_stack() -> Any: ...
def _get_dispatch_stack_at(idx: _int) -> Any: ...
def _len_torch_dispatch_stack() -> _int: ...

class _InferenceMode(object):
    def __init__(self, mode: _bool) -> None: ...

class _DisableFuncTorch:
    def __init__(self) -> None: ...

class _EnableTorchFunction:
    def __init__(self) -> None: ...

class _MultithreadingEnabled:
    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: ...
    def check(self, test_string: str) -> 'FileCheck': ...
    def check_not(self, test_string: str) -> 'FileCheck': ...
    ...

# 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: Union[bytes, _int], 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_set_logging_stream(stream_name: str) -> None: ...
def _jit_pass_cse(Graph) -> _bool: ...
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/Module.cpp
def _rename_privateuse1_backend(backend: str) -> None: ...

# 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/python_dispatch.cpp

class _DispatchOperatorHandle:
    def schema(self) -> FunctionSchema: ...

class _DispatchModule:
    def def_(self, schema: str, alias: str = "") -> _DispatchModule: ...
    def def_legacy(self, schema: str) -> _DispatchModule: ...
    def def_name_t_t(self, name: str, dispatch: str, debug: str = "default_def_name_t_t") -> _DispatchModule: ...
    def def_schema_t_t(self, schema: str, dispatch: str, alias: str, debug: str = "default_def_schema_t_t") -> _DispatchModule: ...
    def impl_t_t(self, name: str, dispatch: str, debug: str = "impl_t_t") -> _DispatchModule: ...
    def impl_tt_t(self, name: str, dispatch: str, debug: str = "impl_tt_t") -> _DispatchModule: ...
    def impl(self, name: str, dispatch: str, func: Callable) -> _DispatchModule: ...
    def define(self, schema: str, alias: str = "") -> _DispatchModule: ...
    def fallback_fallthrough(self, dispatch: str = "") -> _DispatchModule: ...

def _dispatch_library(kind: str, name: str, dispatch: str, file: str = "", linenum: Any = 0) -> _DispatchModule: ...
def _dispatch_dump(name: str) -> str: ...
def _dispatch_dump_table(name: str) -> str: ...
def _dispatch_check_invariants(name: str) -> None: ...
def _dispatch_check_all_invariants() -> None: ...
def _dispatch_has_kernel(name: str) -> _bool: ...
def _dispatch_has_kernel_for_dispatch_key(name: str, dispatch: _dispatchkey) -> _bool: ...
def _dispatch_has_kernel_for_any_dispatch_key(name: str, dispatch_key_set: DispatchKeySet) -> _bool: ...
def _dispatch_has_computed_kernel_for_dispatch_key(name: str, dispatch: _dispatchkey) -> _bool: ...
def _dispatch_find_dangling_impls() -> List[str]: ...
def _dispatch_get_all_op_names() -> List[str]: ...
def _dispatch_tls_set_dispatch_key_excluded(dispatch: _dispatchkey, val: _bool) -> None: ...
def _dispatch_tls_is_dispatch_key_excluded(dispatch: _dispatchkey) -> _bool: ...
def _dispatch_isTensorSubclassLike(tensor: Tensor) -> _bool: ...
def _dispatch_key_name(dispatch: _dispatchkey) -> str: ...
def _dispatch_key_parse(dispatch: _dispatchkey) -> DispatchKey: ...
def _dispatch_num_backends() -> _int: ...

class DispatchKey(Enum):
    ${dispatch_key_hints}

class DispatchKeySet:
    def __or__(self, other: DispatchKeySet) -> DispatchKeySet: ...
    def __sub__(self, other: DispatchKeySet) -> DispatchKeySet: ...
    def __and__(self, other: DispatchKeySet) -> DispatchKeySet: ...
    def highestPriorityTypeId(self) -> DispatchKey: ...
    def has(self, k: _dispatchkey) -> _bool: ...
    def __repr__(self) -> str: ...

_dispatch_autogradother_backends: DispatchKeySet
def _dispatch_has_backend_fallback(dispatch: _dispatchkey) -> _bool: ...
def _dispatch_keyset_full_after(t: _dispatchkey) -> DispatchKeySet: ...
def _dispatch_keyset_to_string(keyset: DispatchKeySet) -> str: ...
def _dispatch_get_backend_keyset_from_autograd(dispatch: _dispatchkey) -> DispatchKeySet: ...
def _dispatch_keys(tensor: Tensor) -> DispatchKeySet: ...
def _dispatch_tls_local_exclude_set() -> DispatchKeySet: ...
def _dispatch_tls_local_include_set() -> DispatchKeySet: ...
def _dispatch_is_included_in_alias(dispatch_a: _dispatchkey, dispatch_b: _dispatchkey) -> _bool: ...

class ExcludeDispatchKeyGuard:
    pass

class _AutoDispatchBelowAutograd:
    pass

def _dispatch_print_registrations_for_dispatch_key(dispatch_key: str = "") -> None: ...
def _dispatch_get_registrations_for_dispatch_key(dispatch_key: str = "") -> List[str]: ...

def _are_functorch_transforms_active() -> _bool: ...

# Define in torch/csrc/autograd/init.cpp
class _DisablePythonDispatcher(object):
    pass

class _EnablePythonDispatcher(object):
    pass

def _set_python_dispatcher(dispatcher: object) -> None: ...


# 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: ...

# Defined in torch/csrc/Storage.cpp
${legacy_storage_base_hints}

# TODO: where
${legacy_class_hints}

# 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
    H: Tensor
    mT: Tensor
    mH: Tensor
    ndim: _int
    output_nr: _int
    _version: _int
    _base: Optional[Tensor]
    _cdata: _int
    grad_fn: Any
    _grad_fn: Any
    _grad: Optional[Tensor]
    grad: Optional[Tensor]
    _backward_hooks: Optional[Dict[_int, Callable[[Tensor], Optional[Tensor]]]]
    ${tensor_method_hints}

# 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_getCurrentRawStream(device: _int) -> _int: ...
def _cuda_getDefaultStream(device: _int) -> _int: ...
def _cuda_getCurrentBlasHandle() -> _int: ...
def _cuda_clearCublasWorkspaces() -> None: ...
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_cudaCachingAllocator_set_allocator_settings(env: str) -> 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() -> Dict[str, Any]: ...
def _cuda_recordMemoryHistory(enabled: _bool, record_context: _bool, record_context_cpp: _bool, alloc_trace_max_entries: _int, alloc_trace_record_context: _bool) -> None: ...
def _cuda_getAllocatorBackend() -> str: ...
def _cuda_lock_mutex() -> None: ...
def _cuda_unlock_mutex() -> None: ...
def _cuda_canDeviceAccessPeer(device: _int, peer_device: _int) -> _bool: ...
def _cuda_jiterator_compile_and_launch_kernel(code_string: str,
                                              kernel_name: str,
                                              return_by_ref: _bool,
                                              num_outputs: _int,
                                              tensors: Tuple,
                                              kwargs: Dict[str, Union[_int, _float, _bool]]) -> Tensor: ...
def _cuda_get_cudnn_benchmark_limit() -> _int: ...
def _cuda_set_cudnn_benchmark_limit(arg: _int) -> None: ...
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: ...
def _rocm_is_backward_pass() -> _bool: ...


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 _cuda_isCurrentStreamCapturing() -> _bool: ...

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: ...
    def with_dtype(self, dtype: _dtype) -> JitType: ...
    def with_sizes(self, sizes: List[Optional[_int]]) -> JitType: ...
    def kind(self) -> str: ...
    def scalarType(self) -> Optional[str]: ...

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 NumberType(JitType):
    @staticmethod
    def get() -> NumberType: ...

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]]: ...
    def varyingSizes(self) -> Optional[List[Optional[_int]]]: ...
    def strides(self) -> Optional[List[_int]]: ...
    def device(self) -> Optional[_device]: ...
    def dim(self) -> _int: ...
    def dtype(self) -> Optional[_dtype]: ...
    @staticmethod
    def create_from_tensor(t: Tensor) -> TensorType: ...

# 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: ...
def _register_py_class_for_device(device: str, cls: Any) -> None: ...
def _activate_cuda_trace() -> None: ...

# Defined in torch/csrc/Module.cpp
def _current_graph_task_id() -> _int: ...

class _OutOfMemoryError:
    pass