code: def parse_args():
      parser = argparse.ArgumentParser()
  
      parser.add_argument(
          "--input",
          type=str,
          default="data/amazon",
          help="Input dataset path",
      )
  
      parser.add_argument(
          "--features",
          type=str,
          default=None,
          help="Input node features",
      )
  
      parser.add_argument(
          "--epoch",
          type=int,
          default=100,
          help="Number of epoch. Default is 100.",
      )
  
      parser.add_argument(
          "--batch-size",
          type=int,
          default=64,
          help="Number of batch_size. Default is 64.",
      )
  
      parser.add_argument(
          "--eval-type",
          type=str,
          default="all",
          help="The edge type(s) for evaluation.",
      )
  
      parser.add_argument(
          "--schema",
          type=str,
          default=None,
          help="The metapath schema (e.g., U-I-U,I-U-I).",
      )
  
      parser.add_argument(
          "--dimensions",
          type=int,
          default=200,
          help="Number of dimensions. Default is 200.",
      )
  
      parser.add_argument(
          "--edge-dim",
          type=int,
          default=10,
          help="Number of edge embedding dimensions. Default is 10.",
      )
  
      parser.add_argument(
          "--att-dim",
          type=int,
          default=20,
          help="Number of attention dimensions. Default is 20.",
      )
  
      parser.add_argument(
          "--walk-length",
          type=int,
          default=10,
          help="Length of walk per source. Default is 10.",
      )
  
      parser.add_argument(
          "--num-walks",
          type=int,
          default=20,
          help="Number of walks per source. Default is 20.",
      )
  
      parser.add_argument(
          "--window-size",
          type=int,
          default=5,
          help="Context size for optimization. Default is 5.",
      )
  
      parser.add_argument(
          "--negative-samples",
          type=int,
          default=5,
          help="Negative samples for optimization. Default is 5.",
      )
  
      parser.add_argument(
          "--neighbor-samples",
          type=int,
          default=10,
          help="Neighbor samples for aggregation. Default is 10.",
      )
  
      parser.add_argument(
          "--patience",
          type=int,
          default=5,
          help="Early stopping patience. Default is 5.",
      )
  
      parser.add_argument(
          "--gpu",
          type=str,
          default=None,
          help="Comma separated list of GPU device IDs.",
      )
  
      parser.add_argument(
          "--workers",
          type=int,
          default=4,
          help="Number of workers.",
      )
  
      return parser.parse_args()
  
file_path: dgl/examples/pytorch/GATNE-T/src/utils.py
imports: import argparse
  import multiprocessing
  import time
  from collections import defaultdict
  from functools import partial, reduce, wraps
  import networkx
  import numpy
  import torch
  from gensim.models.keyedvectors import Vocab
  from six import iteritems
  from sklearn.metrics import auc, f1_score, precision_recall_curve, roc_auc_score
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: parser