code: def load_checkpoint_files(files: Tuple[str, str]):
      """
      ### Load a pair of checkpoint files
  
      :param files: pair of files to load
      :return: the loaded parameter tensors
      """
      checkpoint_path = (
          get_checkpoints_download_path() / "global_step150000"
      )
      with monit.section("Load checkpoint"):
          data = [torch.load(checkpoint_path / f) for f in files]
  
      return data
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/neox/checkpoint.py
imports: from pathlib import Path
  from typing import Dict, Union, Tuple, Optional
  import torch
  from torch import nn
  from labml import monit, lab, logger
  from labml.logger import Text, inspect
  from labml.utils.download import download_file
label_desc: Use image node_id 4 for calling a global function with example usage: load_checkpoint_files(files) and returns: data