code: def save_npz_dict(save_list=None, name='model.npz', sess=None):
      
      if sess is None:
          raise ValueError("session is None.")
      if save_list is None:
          save_list = []
  
      save_list_names = [tensor.name for tensor in save_list]
      save_list_var = sess.run(save_list)
      save_var_dict = {save_list_names[idx]: val for idx, val in enumerate(save_list_var)}
      np.savez(name, **save_var_dict)
      save_list_var = None
      save_var_dict = None
      del save_list_var
      del save_var_dict
      logging.info("[*] Model saved in npz_dict %s" % name)
docs: Input parameters and the file name, save parameters as a dictionary into .npz file.
  
      Use ``tl.files.load_and_assign_npz_dict()`` to restore.
  
      Parameters
      ----------
      save_list : list of parameters
          A list of parameters (tensor) to be saved.
      name : str
          The name of the `.npz` file.
      sess : Session
          TensorFlow Session.