code: def run_predict_epoch(
      py_reader,
      exe,
      program,
      prefix,
      model_dict,
      num_nodes,
      hidden_size,
      log_per_step=100,
  ):
      """run_predict_epoch"""
      batch = 0
      start = time.time()
      # use the parallel executor to speed up
      batch_end = time.time()
      all_feat = np.zeros((num_nodes, hidden_size), dtype="float32")
  
      for batch_feed_dict in tqdm.tqdm(py_reader()):
          batch_start = time.time()
          batch += 1
          batch_src_feat, batch_id = exe.run(
              program,
              feed=batch_feed_dict,
              fetch_list=[model_dict.src_feat.name, model_dict.id.name],
          )
  
          for ind, id in enumerate(batch_id):
              all_feat[id] = batch_src_feat[ind]
      np.save("emb.npy", all_feat)
  
imports: import argparse
  import time
  import glob
  import os
  import numpy
  import pgl
  from pgl.utils.logger import log
  from pgl.utils import paddle_helper
  import paddle
  import paddle.fluid
  import tqdm
  import reader
  import model
label_desc: Use image node_id 6 for calling a global function with example usage: run_predict_epoch(py_reader, exe, program, prefix, model_dict, num_nodes, hidden_size, log_per_step) without return types