code: def parse_args():
      parser = argparse.ArgumentParser(
          description="A script does a multi-gpu training on a GraphSAGE model "
          "for node classification using GraphBolt dataloader."
      )
      parser.add_argument(
          "--gpu",
          type=str,
          default="0",
          help="GPU(s) in use. Can be a list of gpu ids for multi-gpu training,"
          " e.g., 0,1,2,3.",
      )
      parser.add_argument(
          "--epochs",
          type=int,
          default=10,
          help="Number of training epochs.",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=0.001,
          help="Learning rate for optimization.",
      )
      parser.add_argument(
          "--batch-size",
          type=int,
          default=1024,
          help="Batch size for training.",
      )
      parser.add_argument(
          "--fanout",
          type=str,
          default="10,10,10",
          help="Fan-out of neighbor sampling. It is IMPORTANT to keep len(fanout)"
          " identical with the number of layers in your model. Default: 15,10,5",
      )
      parser.add_argument(
          "--num-workers",
          type=int,
          default=0,
          help="The number of processes.",
      )
      return parser.parse_args()
  
imports: import argparse
  import os
  import time
  import dgl.graphbolt
  import dgl.nn
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  import tqdm
  from torch.distributed.algorithms.join import Join
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 5 for calling a global function with example usage: parse_args() and returns: parser