code: def parse_args(args=None):
      parser = argparse.ArgumentParser(
          description="Training and Testing Knowledge Graph Embedding Models",
          usage="train.py [<args>] [-h | --help]",
      )
  
      parser.add_argument("--cuda", action="store_true", help="use GPU")
  
      parser.add_argument("--do_train", action="store_true")
      parser.add_argument("--do_valid", action="store_true")
      parser.add_argument("--do_test", action="store_true")
      parser.add_argument(
          "--evaluate_train",
          action="store_true",
          help="Evaluate on training data",
      )
  
      parser.add_argument(
          "--dataset",
          type=str,
          default="ogbl-biokg",
          help="dataset name, default to biokg",
      )
      parser.add_argument("--model", default="TransE", type=str)
      parser.add_argument(
          "-de", "--double_entity_embedding", action="store_true"
      )
      parser.add_argument(
          "-dr", "--double_relation_embedding", action="store_true"
      )
  
      parser.add_argument(
          "-n", "--negative_sample_size", default=128, type=int
      )
      parser.add_argument("-d", "--hidden_dim", default=500, type=int)
      parser.add_argument("-g", "--gamma", default=12.0, type=float)
      parser.add_argument(
          "-adv", "--negative_adversarial_sampling", action="store_true"
      )
      parser.add_argument(
          "-a", "--adversarial_temperature", default=1.0, type=float
      )
      parser.add_argument("-b", "--batch_size", default=1024, type=int)
      parser.add_argument(
          "-r", "--regularization", default=0.0, type=float
      )
      parser.add_argument(
          "--test_batch_size",
          default=4,
          type=int,
          help="valid/test batch size",
      )
      parser.add_argument(
          "--uni_weight",
          action="store_true",
          help="Otherwise use subsampling weighting like in word2vec",
      )
  
      parser.add_argument(
          "-lr", "--learning_rate", default=0.0001, type=float
      )
      parser.add_argument("-cpu", "--cpu_num", default=10, type=int)
      parser.add_argument(
          "-init", "--init_checkpoint", default=None, type=str
      )
      parser.add_argument(
          "-save", "--save_path", default=None, type=str
      )
      parser.add_argument("--max_steps", default=100000, type=int)
      parser.add_argument("--warm_up_steps", default=None, type=int)
  
      parser.add_argument(
          "--save_checkpoint_steps", default=10000, type=int
      )
      parser.add_argument("--valid_steps", default=10000, type=int)
      parser.add_argument(
          "--log_steps",
          default=100,
          type=int,
          help="train log every xx steps",
      )
      parser.add_argument(
          "--test_log_steps",
          default=1000,
          type=int,
          help="valid/test log every xx steps",
      )
  
      parser.add_argument(
          "--nentity", type=int, default=0, help="DO NOT MANUALLY SET"
      )
      parser.add_argument(
          "--nrelation", type=int, default=0, help="DO NOT MANUALLY SET"
      )
  
      parser.add_argument(
          "--print_on_screen",
          action="store_true",
          help="log on screen or not",
      )
      parser.add_argument(
          "--ntriples_eval_train",
          type=int,
          default=200000,
          help="number of training triples to evaluate eventually",
      )
      parser.add_argument(
          "--neg_size_eval_train",
          type=int,
          default=500,
          help="number of negative samples when evaluating training triples",
      )
      return parser.parse_args(args)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import argparse
  import json
  import logging
  import os
  import random
  import numpy
  import torch
  from torch.utils.data import DataLoader
  from model import KGEModel
  from dataloader import TrainDataset
  from dataloader import BidirectionalOneShotIterator
  from ogb.linkproppred import LinkPropPredDataset, Evaluator
  from collections import defaultdict
  from tqdm.auto import tqdm
  import time
  from tensorboardX import SummaryWriter
  import pdb
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args(args) and returns: parser