code: def read_inference_input_examples_file(input_examples_file):
      """
      read inference input examples file
  
      Args:
          input_examples_file(str): inference input file containing a vector of target word
  
      Returns:
          list(str): target word vector
  
      """
      with open(input_examples_file, "rU", encoding="utf-8") as file:
          reader = csv.reader((line.replace("\0", "") for line in file))
          cntr2 = 0
          # 1. read csv file
          target_word_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(cntr2, line[0])
  
                  cntr2 = cntr2 + 1
  
      return target_word_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 2 for calling a global function with example usage: read_inference_input_examples_file(input_examples_file) and returns: target_word_vec1