code: def export(
      f: Callable,
      args: Tuple[Any, ...],
      kwargs: Optional[Dict[str, Any]] = None,
      *,
      constraints: Optional[List[Constraint]] = None,
      dynamic_shapes: Optional[
          Union[Dict[str, Any], Tuple[Any]]
      ] = None,
      strict: bool = True,
      preserve_module_call_signature: Tuple[str, ...] = (),
  ) -> ExportedProgram:
      """
      :func:`export` takes an arbitrary Python callable (an nn.Module, a function or
      a method) along with example inputs, and produces a traced graph representing
      only the Tensor computation of the function in an Ahead-of-Time (AOT) fashion,
      which can subsequently be executed with different inputs or serialized.  The
      traced graph (1) produces normalized operators in the functional ATen operator set
      (as well as any user-specified custom operators), (2) has eliminated all Python control
      flow and data structures (with certain exceptions), and (3) records the set of
      shape constraints needed to show that this normalization and control-flow elimination
      is sound for future inputs.
  
      **Soundness Guarantee**
  
      While tracing, :func:`export()` takes note of shape-related assumptions
      made by the user program and the underlying PyTorch operator kernels.
      The output :class:`ExportedProgram` is considered valid only when these
      assumptions hold true.
  
      Tracing makes assumptions on the shapes (not values) of input tensors.
      Such assumptions must be validated at graph capture time for :func:`export`
      to succeed. Specifically:
  
      - Assumptions on static shapes of input tensors are automatically validated without additional effort.
      - Assumptions on dynamic shape of input tensors require explicit specification
        by using the :func:`Dim` API to construct dynamic dimensions and by associating
        them with example inputs through the ``dynamic_shapes`` argument.
  
      If any assumption can not be validated, a fatal error will be raised. When that happens,
      the error message will include suggested fixes to the specification that are needed
      to validate the assumptions. For example :func:`export` might suggest the
      following fix to the definition of a dynamic dimension ``dim0_x``, say appearing in the
      shape associated with input ``x``, that was previously defined as ``Dim("dim0_x")``::
  
          dim = Dim("dim0_x", max=5)
  
      This example means the generated code requires dimension 0 of input ``x`` to be less
      than or equal to 5 to be valid. You can inspect the suggested fixes to dynamic dimension
      definitions and then copy them verbatim into your code without needing to change the
      ``dynamic_shapes`` argument to your :func:`export` call.
  
      Args:
          f: The callable to trace.
  
          args: Example positional inputs.
  
          kwargs: Optional example keyword inputs.
  
          constraints: [DEPRECATED: use ``dynamic_shapes`` instead, see below]
           An optional list of constraints on the dynamic arguments
           that specify their possible range of shapes. By default, shapes of
           input torch.Tensors are assumed to be static. If an input torch.Tensor
           is expected to have dynamic shapes, please use :func:`dynamic_dim`
           to define :class:`Constraint` objects that specify the dynamics and the possible
           range of shapes. See :func:`dynamic_dim` docstring for examples on
           how to use it.
  
          dynamic_shapes: Should either be:
           1) a dict from argument names of ``f`` to their dynamic shape specifications,
           2) a tuple that specifies dynamic shape specifications for each input in original order.
           If you are specifying dynamism on keyword args, you will need to pass them in the order that
           is defined in the original function signature.
  
           The dynamic shape of a tensor argument can be specified as either
           (1) a dict from dynamic dimension indices to :func:`Dim` types, where it is
           not required to include static dimension indices in this dict, but when they are,
           they should be mapped to None; or (2) a tuple / list of :func:`Dim` types or None,
           where the :func:`Dim` types correspond to dynamic dimensions, and static dimensions
           are denoted by None. Arguments that are dicts or tuples / lists of tensors are
           recursively specified by using mappings or sequences of contained specifications.
  
          strict: When enabled (default), the export function will trace the program through
           TorchDynamo which will ensure the soundness of the resulting graph. Otherwise, the
           exported program will not validate the implicit assumptions baked into the graph and
           may cause behavior divergence between the original model and the exported one. This is
           useful when users need to workaround bugs in the tracer, or simply want incrementally
           enable safety in their models. Note that this does not affect the resulting IR spec
           to be different and the model will be serialized in the same way regardless of what value
           is passed here.
           WARNING: This option is experimental and use this at your own risk.
  
      Returns:
          An :class:`ExportedProgram` containing the traced callable.
  
      **Acceptable input/output types**
  
      Acceptable types of inputs (for ``args`` and ``kwargs``) and outputs include:
  
      - Primitive types, i.e. ``torch.Tensor``, ``int``, ``float``, ``bool`` and ``str``.
      - Dataclasses, but they must be registered by calling :func:`register_dataclass` first.
      - (Nested) Data structures comprising of ``dict``, ``list``, ``tuple``, ``namedtuple`` and
        ``OrderedDict`` containing all above types.
  
      """
      from ._trace import _export
      from .dynamic_shapes import _process_dynamic_shapes
  
      if constraints is not None:
          warnings.warn(
              "Using `constraints` to specify dynamic shapes for export is DEPRECATED "
              "and will not be supported in the future. "
              "Please use `dynamic_shapes` instead (see docs on `torch.export.export`).",
              DeprecationWarning,
              stacklevel=2,
          )
      else:
          constraints = _process_dynamic_shapes(
              f, args, kwargs, dynamic_shapes
          )
  
      return _export(
          f,
          args,
          kwargs,
          constraints,
          strict=strict,
          preserve_module_call_signature=preserve_module_call_signature,
      )
  
imports: import builtins
  import copy
  import dataclasses
  import inspect
  import io
  import os
  import sys
  import typing
  import warnings
  from enum import auto, Enum
  from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Type, TYPE_CHECKING, Union
  import torch
  import torch.fx._pytree
  import torch.utils._pytree
  from torch.fx._compatibility import compatibility
  from torch.fx.passes.infra.pass_base import PassResult
  from torch.fx.passes.infra.pass_manager import PassManager
  from torch.utils._pytree import FlattenFunc, FromDumpableContextFn, ToDumpableContextFn, UnflattenFunc
  from .dynamic_shapes import Constraint, Dim, dims, dynamic_dim
  from .exported_program import ExportedProgram, ModuleCallEntry, ModuleCallSignature
  from .graph_signature import ExportBackwardSignature, ExportGraphSignature
  from .unflatten import FlatArgsAdapter, unflatten, UnflattenedModule
label_desc: Use image node_id 1 for calling a global function with example usage: export(f, args, kwargs) and returns: _export