code: def main():
      print("Nano Pytorch cat-vs-dog example")
      args = parser.parse_args()
  
      if args.nano_data:
          from bigdl.nano.pytorch.vision.datasets import ImageFolder
          from bigdl.nano.pytorch.vision import transforms
      else:
          from torchvision.datasets import ImageFolder
          from torchvision import transforms
  
      # get dataset
      download_and_extract_archive(url=DATA_URL, download_root="data")
  
      train_loader, val_loader, (train_size, _) = create_data_loader(
          "data", args.batch_size, args.nproc
      )
  
      _main_process(args, train_loader, val_loader, train_size)
  
file_path: BigDL/python/nano/benchmark/resnet50/pytorch-cat-dog.py
imports: import os
  import json
  import torch
  import time
  from bigdl.nano.pytorch.vision.datasets import ImageFolder
  from bigdl.nano.pytorch.vision.transforms import transforms
  import argparse
  from torch.utils.data import DataLoader
  from torchvision.datasets.utils import download_and_extract_archive
  from bigdl.nano.pytorch.vision.models import resnet50
  from bigdl.nano.pytorch.vision.models import ImageClassifier
  from bigdl.nano.pytorch import Trainer
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types