code: def train():
      """
      ## Train Cycle GAN
      """
      # Create configurations
      conf = Configs()
      # Create an experiment
      experiment.create(name="cycle_gan")
      # Calculate configurations.
      # It will calculate `conf.run` and all other configs required by it.
      experiment.configs(
          conf, {"dataset_name": "summer2winter_yosemite"}
      )
      conf.initialize()
  
      # Register models for saving and loading.
      # `get_modules` gives a dictionary of `nn.Modules` in `conf`.
      # You can also specify a custom dictionary of models.
      experiment.add_pytorch_models(get_modules(conf))
      # Start and watch the experiment
      with experiment.start():
          # Run the training
          conf.run()
  
imports: import itertools
  import random
  import zipfile
  from typing import Tuple
  import torch
  import torch.nn
  import torchvision.transforms
  from PIL import Image
  from torch.utils.data import DataLoader, Dataset
  from torchvision.transforms import InterpolationMode
  from torchvision.utils import make_grid
  from labml import lab, tracker, experiment, monit
  from labml.configs import BaseConfigs
  from labml.utils.download import download_file
  from labml.utils.pytorch import get_modules
  from labml_helpers.device import DeviceConfigs
  from labml_helpers.module import Module
label_desc: Use image node_id 3 for calling a global function with example usage: train() without return types