code: def parse_args():
      parser = ArgumentParser(
          formatter_class=ArgumentDefaultsHelpFormatter,
          conflict_handler="resolve",
      )
      parser.add_argument(
          "--input", required=True, help="Input graph file"
      )
      parser.add_argument("--output", help="Output representation file")
      parser.add_argument(
          "--number-walks",
          default=10,
          type=int,
          help="Number of random walks to start at each node",
      )
      parser.add_argument(
          "--directed",
          action="store_true",
          help="Treat graph as directed.",
      )
      parser.add_argument(
          "--walk-length",
          default=80,
          type=int,
          help="Length of the random walk started at each node",
      )
      parser.add_argument(
          "--workers",
          default=8,
          type=int,
          help="Number of parallel processes.",
      )
      parser.add_argument(
          "--representation-size",
          default=128,
          type=int,
          help="Number of latent dimensions to learn for each node.",
      )
      parser.add_argument(
          "--window-size",
          default=10,
          type=int,
          help="Window size of skipgram model.",
      )
      parser.add_argument(
          "--epochs",
          default=5,
          type=int,
          help="The training epochs of LINE and GCN",
      )
      parser.add_argument("--p", default=1.0, type=float)
      parser.add_argument("--q", default=1.0, type=float)
      parser.add_argument(
          "--method",
          required=True,
          choices=[
              "node2vec",
              "deepWalk",
              "line",
              "gcn",
              "grarep",
              "tadw",
              "lle",
              "hope",
              "lap",
              "gf",
              "sdne",
          ],
          help="The learning method",
      )
      parser.add_argument(
          "--label-file", default="", help="The file of node label"
      )
      parser.add_argument(
          "--feature-file", default="", help="The file of node features"
      )
      parser.add_argument(
          "--graph-format",
          default="adjlist",
          choices=["adjlist", "edgelist"],
          help="Input graph format",
      )
      parser.add_argument(
          "--negative-ratio",
          default=5,
          type=int,
          help="the negative ratio of LINE",
      )
      parser.add_argument(
          "--weighted",
          action="store_true",
          help="Treat graph as weighted",
      )
      parser.add_argument(
          "--clf-ratio",
          default=0.5,
          type=float,
          help="The ratio of training data in the classification",
      )
      parser.add_argument(
          "--order",
          default=3,
          type=int,
          help="Choose the order of LINE, 1 means first order, 2 means second order, 3 means first order + second order",
      )
      parser.add_argument(
          "--no-auto-save",
          action="store_true",
          help="no save the best embeddings when training LINE",
      )
      parser.add_argument(
          "--dropout",
          default=0.5,
          type=float,
          help="Dropout rate (1 - keep probability)",
      )
      parser.add_argument(
          "--weight-decay",
          type=float,
          default=5e-4,
          help="Weight for L2 loss on embedding matrix",
      )
      parser.add_argument(
          "--hidden",
          default=16,
          type=int,
          help="Number of units in hidden layer 1",
      )
      parser.add_argument(
          "--kstep",
          default=4,
          type=int,
          help="Use k-step transition probability matrix",
      )
      parser.add_argument(
          "--lamb",
          default=0.2,
          type=float,
          help="lambda is a hyperparameter in TADW",
      )
      parser.add_argument(
          "--lr", default=0.01, type=float, help="learning rate"
      )
      parser.add_argument(
          "--alpha",
          default=1e-6,
          type=float,
          help="alhpa is a hyperparameter in SDNE",
      )
      parser.add_argument(
          "--beta",
          default=5.0,
          type=float,
          help="beta is a hyperparameter in SDNE",
      )
      parser.add_argument(
          "--nu1",
          default=1e-5,
          type=float,
          help="nu1 is a hyperparameter in SDNE",
      )
      parser.add_argument(
          "--nu2",
          default=1e-4,
          type=float,
          help="nu2 is a hyperparameter in SDNE",
      )
      parser.add_argument(
          "--bs", default=200, type=int, help="batch size of SDNE"
      )
      parser.add_argument(
          "--encoder-list",
          default="[1000, 128]",
          type=str,
          help="a list of numbers of the neuron at each encoder layer, the last number is the "
          "dimension of the output node representation",
      )
      args = parser.parse_args()
  
      if args.method != "gcn" and not args.output:
          print("No output filename. Exit.")
          exit(1)
  
      return args
  
imports: from __future__ import print_function
  import numpy
  import random
  from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
  from sklearn.linear_model import LogisticRegression
  from .graph import *
  from .None import node2vec
  from .classify import Classifier, read_node_label
  from .None import line
  from .None import tadw
  from .gcn import gcnAPI
  from .None import lle
  from .None import hope
  from .None import lap
  from .None import gf
  from .None import sdne
  from .grarep import GraRep
  import time
  import ast
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args