code: def graph_dumper_aot(
      current_name, folder_name, dump_example_input=False
  ):
      """
      Dump the forward, backward, and joint computation graph.
      Example Usage:
      save_fx_func = graph_dumper_aot(current_name, folder_name, dump_example_input = False)
      optimize_ctx = torchdynamo.optimize(
          save_fx_func
      )
      with torch.enable_grad():
          with optimize_ctx:
              result = forward_and_backward_pass(model, example_inputs)
      """
      global graph_index
      graph_index = 0
      return partial(
          _save_fx_default,
          current_name,
          folder_name,
          dump_example_input,
      )
  
imports: import copy
  import logging
  import os
  import pickle
  import random
  from contextlib import contextmanager
  from functools import partial
  from typing import Callable, Union
  import sympy
  import torch
  from torch import SymInt
  import torch.fx
  import torch.nn
  from torch._decomp import get_decompositions
  from torch.fx.experimental.symbolic_shapes import bind_symbols
  from .aot_autograd import aot_function, aot_module, make_boxed_compiler
  from .compile_utils import strip_overloads
  from .partitioners import default_partition, draw_graph, min_cut_rematerialization_partition
  import torch.utils._pytree
label_desc: Use image node_id 15 for calling a global function with example usage: graph_dumper_aot(current_name, folder_name, dump_example_input) and returns: partial