code: def set_checkpoint(model, opt, progress_saver, savepath):
      """
      Store relevant parameters (model and progress saver, as well as parameter-namespace).
      Can be easily extend for other stuff.
  
      Args:
          model:          PyTorch network, network whose parameters are to be saved.
          opt:            argparse.Namespace, includes all training-specific parameters
          progress_saver: subclass of LOGGER-class, contains a running memory of all training metrics.
          savepath:       str, where to save checkpoint.
      Returns:
          Nothing!
      """
      torch.save(
          {
              "state_dict": model.state_dict(),
              "opt": opt,
              "progress": progress_saver,
          },
          savepath,
      )
  
imports: import warnings
  import csv
  import datetime
  import os
  import pickle
  import faiss
  import matplotlib.pyplot
  import numpy
  import torch
  from PIL import Image
  from sklearn import metrics
  from torch import nn
  from tqdm import tqdm
  import pdb
label_desc: Use image node_id 9 for calling a global function with example usage: set_checkpoint(model, opt, progress_saver, savepath) without return types