code: def get_config():
      parser = argparse.ArgumentParser()
  
      ## model_arg
      model_group = parser.add_argument_group("model_base_arg")
      model_group.add_argument("--num_layers", default=3, type=int)
      model_group.add_argument("--hidden_size", default=128, type=int)
      model_group.add_argument("--num_heads", default=2, type=int)
      model_group.add_argument("--dropout", default=0.3, type=float)
      model_group.add_argument("--attn_dropout", default=0, type=float)
  
      ## label_embed_arg
      embed_group = parser.add_argument_group("embed_arg")
      embed_group.add_argument("--use_label_e", action="store_true")
      embed_group.add_argument(
          "--label_rate", default=0.625, type=float
      )
  
      ## train_arg
      train_group = parser.add_argument_group("train_arg")
      train_group.add_argument("--runs", default=10, type=int)
      train_group.add_argument("--epochs", default=2000, type=int)
      train_group.add_argument("--lr", default=0.001, type=float)
      train_group.add_argument("--place", default=-1, type=int)
      train_group.add_argument(
          "--log_file", default="result_arxiv.txt", type=str
      )
      return parser.parse_args()
  
file_path: PGL/ogb_examples/nodeproppred/unimp/main_arxiv.py
imports: import math
  import torch
  import paddle
  import pgl
  import numpy
  import paddle.fluid
  import paddle.fluid.layers
  from pgl.contrib.ogb.nodeproppred.dataset_pgl import PglNodePropPredDataset
  from ogb.nodeproppred import Evaluator
  from utils import to_undirected, add_self_loop, linear_warmup_decay
  from model import Arxiv_baseline_model, Arxiv_label_embedding_model
  import argparse
  from tqdm import tqdm
label_desc: Use image node_id 1 for calling a global function with example usage: get_config() and returns: parser