code: def create_squad_training(
      paras_file, ques_file, answer_file, data_train_len=None
  ):
      """
      Function to read data from preprocessed files and return
      data in the form of a list
  
      Args:
      -----------
      paras_file: File name for preprocessed paragraphs
      ques_file: File name for preprocessed questions
      answer_file: File name for preprocessed answer spans
      vocab_file:  File name for preprocessed vocab
      data_train_len= length of train dataset to use
  
      Returns:
      --------
      appended list for train/dev dataset
      """
  
      para_list = []
      ques_list = []
      ans_list = []
      with open(paras_file) as f_para:
          for ele in f_para:
              para_list.append(list(map(int, ele.strip().split())))
  
      with open(ques_file) as f_ques:
          for ele in f_ques:
              ques_list.append(list(map(int, ele.strip().split())))
  
      with open(answer_file) as f_ans:
          for ele in f_ans:
              ans_list.append(list(map(int, ele.strip().split())))
  
      data_train = []
      if data_train_len is None:
          data_train_len = len(para_list)
  
      for idx in range(data_train_len):
          data_train.append(
              [para_list[idx], ques_list[idx], ans_list[idx]]
          )
  
      return data_train
  
imports: import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: create_squad_training(paras_file, ques_file, answer_file, data_train_len) and returns: data_train