code: def save(
      ep: ExportedProgram,
      f: Union[str, os.PathLike, io.BytesIO],
      *,
      extra_files: Optional[Dict[str, Any]] = None,
      opset_version: Optional[Dict[str, int]] = None,
  ) -> None:
      if not isinstance(ep, ExportedProgram):
          raise TypeError(
              f"save() expects an ExportedProgram but got {type(ep)}"
          )
  
      from .serde.serialize import serialize, SerializedArtifact
      from .serde.schema import SCHEMA_VERSION
  
      artifact: SerializedArtifact = serialize(ep, opset_version)
  
      if isinstance(f, (str, os.PathLike)):
          f = os.fspath(f)
  
      with zipfile.ZipFile(f, "w") as zipf:
          # Save every field the SerializedArtifact to a file
          assert isinstance(artifact.exported_program, bytes)
          zipf.writestr(
              "serialized_exported_program.json",
              artifact.exported_program,
          )
          zipf.writestr("serialized_state_dict.pt", artifact.state_dict)
          zipf.writestr("serialized_constants.pt", artifact.constants)
  
          zipf.writestr("version", ".".join(map(str, SCHEMA_VERSION)))
  
          # Add extra files if provided
          if extra_files:
              for extra_file_name, content in extra_files.items():
                  encoded_content = content.encode("utf-8")
                  zipf.writestr(
                      f"extra_files/{extra_file_name}", encoded_content
                  )
  
imports: import copy
  import dataclasses
  import functools
  import io
  import json
  import os
  import re
  import sys
  import types
  import warnings
  import weakref
  import zipfile
  from collections import OrderedDict
  from contextlib import contextmanager
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  from unittest.mock import patch
  import sympy
  import torch
  import torch._dynamo
  import torch.fx
  import torch.fx._pytree
  import torch.utils._pytree
  from torch._decomp import core_aten_decompositions, get_decompositions
  from torch._dispatch.python import enable_python_dispatcher
  from torch._dynamo.exc import UserError, UserErrorType
  from torch._dynamo.source import ConstantSource
  from torch._export.passes.collect_tracepoints_pass import CollectTracepointsPass
  from torch._functorch.aot_autograd import aot_export_module, GraphSignature
  from torch._functorch.eager_transforms import functionalize
  from torch._guards import detect_fake_mode
  from torch._ops import OpOverload
  from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
  from torch._subclasses.functional_tensor import FunctionalTensor
  from torch.export.exported_program import ExportedProgram, ModuleCallEntry, ModuleCallSignature, _disable_prexisiting_fake_mode
  from torch.export.graph_signature import _sig_to_specs, ArgumentSpec, ConstantArgument, ExportGraphSignature, InputKind, InputSpec, OutputKind, OutputSpec, SymIntArgument, TensorArgument
  from torch.export.dynamic_shapes import Constraint, dynamic_dim, _process_constraints, _process_dynamic_shapes
  from torch.export._unlift import _create_stateful_graph_module
  from torch.fx import traceback
  from torch.fx._compatibility import compatibility
  from torch.fx.experimental.proxy_tensor import make_fx, maybe_disable_fake_tensor_mode
  from torch.fx.experimental.symbolic_shapes import ConstraintViolationError, GuardOnDataDependentSymNode, ShapeEnv, StrictMinMaxConstraint
  from torch.fx.graph import _PyTreeCodeGen, _PyTreeInfo
  from torch.utils._sympy.value_ranges import ValueRangeError, ValueRanges
  from .exported_program import CallSpec
  from .passes.add_runtime_assertions_for_constraints_pass import _AddRuntimeAssertionsForInlineConstraintsPass
  from .passes.lift_constant_tensor_pass import lift_constant_tensor_pass
  from .passes.remove_runtime_assertions import _RemoveRuntimeAssertionsPass
  from .passes.replace_sym_size_ops_pass import _replace_sym_size_ops_pass
  from .passes.replace_view_ops_with_view_copy_ops_pass import ReplaceViewOpsWithViewCopyOpsPass
  from .wrappers import _wrap_submodules
  from torch._inductor import config
label_desc: Use image node_id 3 for calling a global function with example usage: save(ep, f) without return types