code: def read_gs_file(gs_file_name):
      """
      reads gold standard file
  
      Args:
          gs_file_name (str): the file path
  
      Returns:
          target_word_vec1 (list(str)): words vector
          definition_vec1  (list(str)): words definitions vector - definition per target word
          hypernym_vec1    (list(str)): words hypernyms vector
          label_vec1       (list(str)): labels of binary class 0/1
      """
      with open(gs_file_name, "rU", encoding="utf-8") as file:
          reader = csv.reader((line.replace("\0", "") for line in file))
  
          cntr1 = 0
          # 1. read csv file
          target_word_vec1 = []
          definition_vec1 = []
          hypernym_vec1 = []
  
          label_vec1 = []
  
          header_line_flag = True
          for line in reader:
              if line is not None:
                  if header_line_flag:  # skip header line
                      header_line_flag = False
                      continue
  
                  target_word_vec1.insert(cntr1, line[0].strip())
                  definition_vec1.insert(cntr1, line[1])
                  hypernym_vec1.insert(cntr1, line[2])
                  label_vec1.insert(cntr1, line[3])
                  cntr1 = cntr1 + 1
  
      return (
          target_word_vec1,
          definition_vec1,
          hypernym_vec1,
          label_vec1,
      )
  
file_path: nlp-architect/examples/most_common_word_sense/prepare_data.py
imports: import argparse
  import csv
  import logging
  import math
  import pickle
  import gensim
  import numpy
  from sklearn.model_selection import train_test_split
  from examples.most_common_word_sense.feature_extraction import extract_features_envelope
  from nlp_architect.utils.io import validate_existing_filepath, check_size, validate_parent_exists
label_desc: Use image node_id 1 for calling a global function with example usage: read_gs_file(gs_file_name) and returns: target_word_vec1,  definition_vec1,  hypernym_vec1,  label_vec1