code: def run(self, shape_env=None) -> Any:
      from torch.fx.experimental.symbolic_shapes import (
          ShapeEnv,
          SymTypes,
      )
  
      # Special handling for the constructor event.
      if self.f is ShapeEnv:
          assert (
              shape_env is None
              and self.args is None
              and self.kwargs is not None
          )
          return ShapeEnv(**self.kwargs)
  
      assert shape_env is not None
      args = list(self.args or list())
      kwargs = dict(self.kwargs or dict())
  
      # Replace any argument of type ShapeEnv by the given one.
      args, kwargs = pytree.tree_map_only(
          ShapeEnv, lambda _: shape_env, (args, kwargs)
      )
  
      # Replace any argument of type SymTypes by a new instance,
      # replacing its ShapeEnv reference.
      args, kwargs = pytree.tree_map_only(
          SymTypes,
          lambda a: type(a)(a.node.with_shape_env(shape_env)),
          (args, kwargs),
      )
  
      # Converts FX nodes using the mapping argument.
      def maybe_convert_node(x: Any) -> Any:
          if not isinstance(x, torch.fx.Node):
              # Don't do anything to x if it's not an FX node.
              return x
          # If, at some point, we created an FX node, it means that translation validation is on.
          # It also means we are building an FX graph for symbolic shapes at shape_env.graph, and
          # we are tracking node names at shape_env.name_to_node.
          assert hasattr(shape_env, "name_to_node")
          name_to_node = shape_env.name_to_node  # type: ignore[attr-defined]
          assert x.name in name_to_node
          return name_to_node[x.name]
  
      # Replaces the value of an specific argument by the result of fn.
      def replacearg(index: int, key: str, fn: Callable):
          if index < len(args):
              args[index] = fn(args[index])
          if key in kwargs:
              kwargs[key] = fn(kwargs[key])
  
      if self.is_create_fx_call_function():
          # ShapeEnv.create_fx_call_function:
          # "args" parameter is a tuple of FX nodes from the FX graph of the old ShapeEnv.
          # They must be replaced, since a "call_function" FX node with this tuple as argument
          # will be added to the FX graph of the new shape_env.
          replacearg(
              index=2,
              key="args",
              fn=lambda args: tuple(
                  maybe_convert_node(a) for a in args
              ),
          )
      if self.is_evaluate_expr() or self.is_defer_runtime_assert():
          # ShapeEnv.evaluate_expr and ShapeEnv.defer_runtime_assert:
          # "fx_node" parameter is an (optional) FX node that represents the evaluate expression.
          # They must be replaced, since it will be part of a "call_function" FX node for
          # torch._assert, which will be added to the FX graph of the new shape_env.
          replacearg(index=3, key="fx_node", fn=maybe_convert_node)
  
      # Actually call the method with the converted arguments.
      return self.f(*args, **kwargs)
  
imports: import functools
  import itertools
  from dataclasses import dataclass
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import torch
  import torch.utils._pytree
label_desc: Use image node_id 1 for calling the ShapeEnvEvent obj's underlying member method code with example usage: obj.run(shape_env) and returns: self, ShapeEnv, name_to_node, x