code: def normalize_module_name(layer_name):
      """Normalize a module's name.
  
      PyTorch let's you parallelize the computation of a model, by wrapping a model with a
      DataParallel module.  Unfortunately, this changs the fully-qualified name of a module,
      even though the actual functionality of the module doesn't change.
      Many time, when we search for modules by name, we are indifferent to the DataParallel
      module and want to use the same module name whether the module is parallel or not.
      We call this module name normalization, and this is implemented here.
      """
      modules = layer_name.split(".")
      try:
          idx = modules.index("module")
      except ValueError:
          return layer_name
      del modules[idx]
      return ".".join(modules)
  
imports: import argparse
  from collections import OrderedDict
  from copy import deepcopy
  import logging
  import operator
  import random
  import numpy
  import torch
  import torch.nn
  import torch.backends.cudnn
  import yaml
  import inspect
label_desc: Use image node_id 9 for calling a global function with example usage: normalize_module_name(layer_name) and returns: str, layer_name