code: def parse_args():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--torch_devices",
          type=str,
          default="0,1",
          help="GPU to allocate to pytorch for model, graph data, and node label storage",
          required=False,
      )
  
      parser.add_argument(
          "--num_epochs",
          type=int,
          default=1,
          help="Number of training epochs",
          required=False,
      )
  
      parser.add_argument(
          "--features_on_gpu",
          type=bool,
          default=True,
          help="Whether to store the features on each worker's GPU",
          required=False,
      )
  
      parser.add_argument(
          "--torch_manager_ip",
          type=str,
          default="127.0.0.1",
          help="The torch distributed manager ip address",
          required=False,
      )
  
      parser.add_argument(
          "--torch_manager_port",
          type=str,
          default="12346",
          help="The torch distributed manager port",
          required=False,
      )
  
      parser.add_argument(
          "--dask_scheduler_file",
          type=str,
          help="The path to the dask scheduler file",
          required=True,
      )
  
      return parser.parse_args()
  
imports: from ogb.nodeproppred import NodePropPredDataset
  import time
  import argparse
  import gc
  import torch
  import numpy
  from torch_geometric.nn import CuGraphSAGEConv
  import torch.nn
  import torch.nn.functional
  import torch.distributed
  import torch.multiprocessing
  from torch.nn.parallel import DistributedDataParallel
  from typing import List
label_desc: Use image node_id 6 for calling a global function with example usage: parse_args() and returns: parser