code: def load_data():
      global embed, char_vcb, tokenizer, word_vcb
      logger.debug("tokenize data")
      tokenizer = data.WhitespaceTokenizer()
  
      char_set = set()
      word_set = set()
      logger.debug("generate train data")
      qp_pairs = generate_data(
          input_file, tokenizer, char_set, word_set, is_training=True
      )
      logger.debug("generate dev data")
      dev_qp_pairs = generate_data(
          dev_file, tokenizer, char_set, word_set, is_training=False
      )
      logger.debug("generate data done.")
  
      char_vcb = {
          char: sample_id for sample_id, char in enumerate(char_set)
      }
      word_vcb = {
          word: sample_id for sample_id, word in enumerate(word_set)
      }
  
      timer.start()
      logger.debug("read embedding table")
  
      cfg.word_embed_dim = 300
      embed = np.zeros(
          (len(word_vcb), cfg.word_embed_dim), dtype=np.float32
      )
  
      embedding = load_embedding(args.embedding_file)
      for word, sample_id in enumerate(word_vcb):
          if word in embedding:
              embed[sample_id] = embedding[word]
  
      # add UNK into dict
      unk = np.zeros((1, cfg.word_embed_dim), dtype=np.float32)
      embed = np.concatenate((unk, embed), axis=0)
      word_vcb = {key: value + 1 for key, value in word_vcb.items()}
  
      return qp_pairs, dev_qp_pairs
  
imports: import argparse
  import heapq
  import json
  import os
  import pickle
  import logging
  import numpy
  from tensorflow.train import init_from_checkpoint
  import graph
  from util import Timer
  import nni
  import data
  import evaluate
  from train_model import *
label_desc: Use image node_id 11 for calling a global function with example usage: load_data() and returns: qp_pairs,  dev_qp_pairs