code: def process(args):
      idx, graph, save_path, epoch, batch_size, walk_len, seed = args
      with open("%s/%s" % (save_path, idx), "w") as outf:
          for _ in range(epoch):
              np.random.seed(seed)
              for nodes in graph.node_batch_iter(batch_size):
                  walks = graph.random_walk(nodes, walk_len)
                  for walk in walks:
                      outf.write(
                          " ".join([str(token) for token in walk])
                          + "\n"
                      )
  
imports: import argparse
  import time
  import math
  import os
  import io
  from multiprocessing import Pool
  import glob
  import numpy
  import sklearn.metrics
  from sklearn.metrics import f1_score
  import pgl
  from pgl import data_loader
  from pgl.utils import op
  from pgl.utils.logger import log
  import paddle.fluid
  import paddle.fluid.layers
label_desc: Use image node_id 5 for calling a global function with example usage: process(args) without return types