code: def download_mnist_cnn():
      import urllib.request
  
      # Download a pre-trained MNIST model for inception score calculation.
      # This is a tiny model (<100kb).
      if not os.path.exists(MODEL_PATH):
          print("downloading model")
          os.makedirs(os.path.dirname(MODEL_PATH), exist_ok=True)
          urllib.request.urlretrieve(
              "https://github.com/ray-project/ray/raw/master/python/ray/tune/"
              "examples/pbt_dcgan_mnist/mnist_cnn.pt",
              MODEL_PATH,
          )
      return MODEL_PATH
  
file_path: ray/python/ray/tune/examples/pbt_dcgan_mnist/pbt_dcgan_mnist_func.py
imports: import ray
  from ray import train, tune
  from ray.train import Checkpoint
  from ray.tune.schedulers import PopulationBasedTraining
  import argparse
  import os
  from filelock import FileLock
  import tempfile
  import torch
  import torch.nn
  import torch.nn.parallel
  import torch.optim
  import torch.utils.data
  import numpy
  from ray.tune.examples.pbt_dcgan_mnist.common import beta1, MODEL_PATH, demo_gan, get_data_loader, plot_images, train_func, weights_init, Discriminator, Generator, Net
label_desc: Use image node_id 2 for calling a global function with example usage: download_mnist_cnn() and returns: MODEL_PATH