code: def main():
      # adds training defaults and support for terminal arguments
      parser = argparse.ArgumentParser(
          description="PyTorch Siamese network Example"
      )
      parser.add_argument(
          "--batch-size",
          type=int,
          default=256,
          metavar="N",
          help="input batch size for training (default: 64)",
      )
      parser.add_argument(
          "--test-batch-size",
          type=int,
          default=256,
          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: 14)",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=1.0,
          metavar="LR",
          help="learning rate (default: 1.0)",
      )
      parser.add_argument(
          "--gamma",
          type=float,
          default=0.95,
          metavar="M",
          help="Learning rate step gamma (default: 0.7)",
      )
      parser.add_argument(
          "--no-cuda",
          action="store_true",
          default=False,
          help="disables CUDA training",
      )
      parser.add_argument(
          "--no-mps",
          action="store_true",
          default=False,
          help="disables macOS GPU training",
      )
      parser.add_argument(
          "--dry-run",
          action="store_true",
          default=False,
          help="quickly check a single pass",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=1,
          metavar="S",
          help="random seed (default: 1)",
      )
      parser.add_argument(
          "--log-interval",
          type=int,
          default=1,
          metavar="N",
          help="how many batches to wait before logging training status",
      )
      parser.add_argument(
          "--save-model",
          action="store_true",
          default=False,
          help="For Saving the current Model",
      )
      parser.add_argument(
          "--num-workers",
          default=4,
          help="number of processes generating parallel batches",
      )
      args = parser.parse_args()
  
      # set manual seed
      manual_seed(args.seed)
  
      # set device
      device = (
          torch.device("cuda")
          if torch.cuda.is_available()
          else torch.device("cpu")
      )
  
      # data loading
      train_dataset = MatcherDataset(
          "../data", train=True, download=True
      )
      test_dataset = MatcherDataset("../data", train=False)
      train_loader = DataLoader(
          train_dataset,
          shuffle=True,
          batch_size=args.batch_size,
          num_workers=args.num_workers,
      )
      test_loader = DataLoader(
          test_dataset,
          batch_size=args.test_batch_size,
          num_workers=args.num_workers,
      )
  
      # set model parameters
      model = SiameseNetwork().to(device)
      optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
      scheduler = StepLR(optimizer, step_size=15, gamma=args.gamma)
      lr_scheduler = LRScheduler(scheduler)
  
      # call run function
      run(
          args,
          model,
          device,
          optimizer,
          train_loader,
          test_loader,
          lr_scheduler,
      )
  
imports: import argparse
  import numpy
  import torch
  import torch.nn
  import torch.optim
  import torchvision
  from torch.optim.lr_scheduler import StepLR
  from torch.utils.data import DataLoader, Dataset
  from torchvision import datasets
  from ignite.contrib.handlers import ProgressBar
  from ignite.engine import Engine, Events
  from ignite.handlers.param_scheduler import LRScheduler
  from ignite.metrics import Accuracy, RunningAverage
  from ignite.utils import manual_seed
label_desc: Use image node_id 4 for calling a global function with example usage: main() without return types