code: def unpack(model, training_config, weights):
      restored_model = deserialize(model)
      if training_config is not None:
          restored_model.compile(
              **saving_utils.compile_args_from_training_config(
                  training_config
              )
          )
      restored_model.set_weights(weights)
      return restored_model
  
file_path: TextAttack/examples/attack/attack_keras_parallel.py
imports: import numpy
  import tensorflow
  from tensorflow.keras.layers import Dense, Dropout
  from tensorflow.keras.models import Model, Sequential
  from tensorflow.keras.utils import to_categorical
  from tensorflow.python.keras.layers import deserialize, serialize
  from tensorflow.python.keras.saving import saving_utils
  import torch
  from textattack import AttackArgs, Attacker
  from textattack.attack_recipes import PWWSRen2019
  from textattack.datasets import HuggingFaceDataset
  from textattack.models.wrappers import ModelWrapper
label_desc: Use image node_id 1 for calling a global function with example usage: unpack(model, training_config, weights) and returns: restored_model