code: def main():
      # Training settings
      parser = argparse.ArgumentParser(
          description="PyTorch MNIST Example"
      )
      parser.add_argument(
          "--batch-size",
          type=int,
          default=512,
          metavar="N",
          help="input batch size for training (default: 64)",
      )
      parser.add_argument(
          "--test-batch-size",
          type=int,
          default=512,
          metavar="N",
          help="input batch size for testing (default: 1000)",
      )
      parser.add_argument(
          "--epochs",
          type=int,
          default=10,
          metavar="N",
          help="number of epochs to train (default: 10)",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=0.01,
          metavar="LR",
          help="learning rate (default: 0.01)",
      )
      parser.add_argument(
          "--no-cuda",
          action="store_true",
          default=False,
          help="disables CUDA training",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=1,
          metavar="S",
          help="random seed (default: 1)",
      )
      parser.add_argument(
          "--log-interval",
          type=int,
          default=10,
          metavar="N",
          help="how many batches to wait before logging training status",
      )
      args = parser.parse_args()
      use_cuda = not args.no_cuda and torch.cuda.is_available()
  
      torch.manual_seed(args.seed)
  
      device = torch.device("cuda" if use_cuda else "cpu")
  
      kwargs = (
          {"num_workers": 1, "pin_memory": True} if use_cuda else {}
      )
      train_loader = torch.utils.data.DataLoader(
          datasets.MNIST(
              "../data",
              train=True,
              download=True,
              transform=transforms.Compose(
                  [
                      transforms.ToTensor(),
                      transforms.Normalize((0.1307,), (0.3081,)),
                  ]
              ),
          ),
          batch_size=args.batch_size,
          shuffle=True,
          **kwargs,
      )
      test_loader = torch.utils.data.DataLoader(
          datasets.MNIST(
              "../data",
              train=False,
              transform=transforms.Compose(
                  [
                      transforms.ToTensor(),
                      transforms.Normalize((0.1307,), (0.3081,)),
                  ]
              ),
          ),
          batch_size=args.test_batch_size,
          shuffle=True,
          **kwargs,
      )
  
      model = Net(device=device).to(device)
      optimizer = optim.Adam(model.parameters(), lr=args.lr)
  
      for epoch in range(1, args.epochs + 1):
          train(args, model, device, train_loader, optimizer, epoch)
          test(args, model, device, test_loader)
  
file_path: dgl/examples/pytorch/capsule/main.py
imports: import argparse
  import torch
  import torch.optim
  from model import Net
  from torchvision import datasets, transforms
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types