code: def __init__(self):
      super(ArgParser, self).__init__()
  
      self.add_argument(
          "--gpu",
          type=int,
          default=[-1],
          nargs="+",
          help="A list of gpu ids, e.g. 0 1 2 4",
      )
      self.add_argument(
          "--mix_cpu_gpu",
          action="store_true",
          help="Training a knowledge graph embedding model with both CPUs and GPUs."
          "The embeddings are stored in CPU memory and the training is performed in GPUs."
          "This is usually used for training a large knowledge graph embeddings.",
      )
      self.add_argument(
          "--valid",
          action="store_true",
          help="Evaluate the model on the validation set in the training.",
      )
      self.add_argument(
          "--rel_part",
          action="store_true",
          help="Enable relation partitioning for multi-GPU training.",
      )
      self.add_argument(
          "--async_update",
          action="store_true",
          help="Allow asynchronous update on node embedding for multi-GPU training."
          "This overlaps CPU and GPU computation to speed up.",
      )
      self.add_argument(
          "--has_edge_importance",
          action="store_true",
          help="Allow providing edge importance score for each edge during training."
          "The positive score will be adjusted "
          "as pos_score = pos_score * edge_importance",
      )
  
      self.add_argument("--print_on_screen", action="store_true")
      self.add_argument(
          "--encoder_model_name",
          type=str,
          default="shallow",
          help="shallow or roberta or concat",
      )
      self.add_argument(
          "--mlp_lr",
          type=float,
          default=0.0001,
          help="The learning rate of optimizing mlp",
      )
      self.add_argument(
          "--seed", type=int, default=0, help="random seed"
      )
  
file_path: ogb/examples/lsc/wikikg90m/dgl-ke-ogb-lsc/python/dglke/train.py
imports: import os
  import logging
  import time
  from .dataloader import EvalDataset, TrainDataset, NewBidirectionalOneShotIterator
  from .dataloader import get_dataset
  from .utils import get_compatible_batch_size, save_model, CommonArgParser
  import torch
  import numpy
  import torch.multiprocessing
  from .train_pytorch import load_model
  from .train_pytorch import train, train_mp
  from .train_pytorch import test, test_mp
  from ogb.lsc import WikiKG90MDataset, WikiKG90MEvaluator
label_desc: Use image node_id 1 to create a new ArgParser object from inherited base classes: CommonArgParser with example: obj = ArgParser()