code: def parse_args():
      parser = argparse.ArgumentParser()
      parser.add_argument("--epochs", type=int, default=10)
      parser.add_argument(
          "--lr",
          type=float,
          default=0.0005,
          help="Learning rate. Default: 0.0005",
      )
      parser.add_argument(
          "--train-batch-size",
          type=int,
          default=512,
          help="Batch size for training. Default: 512",
      )
      parser.add_argument(
          "--eval-batch-size",
          type=int,
          default=1024,
          help="Batch size during evaluation. Default: 1024",
      )
      parser.add_argument(
          "--early-stop",
          type=int,
          default=0,
          help="0 means no early stop, otherwise stop at the input-th step",
      )
      parser.add_argument(
          "--exclude-edges",
          type=int,
          default=1,
          help="Whether to exclude reverse edges during sampling. Default: 1",
      )
      parser.add_argument(
          "--compare-graphbolt",
          action="store_true",
          help="Compare with GraphBolt",
      )
      parser.add_argument(
          "--mode",
          default="mixed",
          choices=["cpu", "mixed", "puregpu"],
          help="Training mode. 'cpu' for CPU training, 'mixed' for CPU-GPU mixed "
          "training, 'puregpu' for pure-GPU training.",
      )
      return parser.parse_args()
  
imports: import argparse
  import time
  import dgl
  import dgl.nn
  import torch
  import torch.nn
  import torch.nn.functional
  import tqdm
  from dgl.dataloading import as_edge_prediction_sampler, DataLoader, MultiLayerFullNeighborSampler, negative_sampler, NeighborSampler
  from ogb.linkproppred import DglLinkPropPredDataset, Evaluator
label_desc: Use image node_id 5 for calling a global function with example usage: parse_args() and returns: parser