code: def denormalize_module_name(parallel_model, normalized_name):
      """Convert back from the normalized form of the layer name, to PyTorch's name
      which contains "artifacts" if DataParallel is used.
      """
      fully_qualified_name = [
          mod_name
          for mod_name, _ in parallel_model.named_modules()
          if normalize_module_name(mod_name) == normalized_name
      ]
      if len(fully_qualified_name) > 0:
          return fully_qualified_name[-1]
      else:
          return normalized_name
  
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 10 for calling a global function with example usage: denormalize_module_name(parallel_model, normalized_name) and returns: fully_qualified_name, normalized_name