code: def parse_generic_config_vars_file(var_file):
      variables = {}
      try:
          var_file_handle = open(var_file, "r")
          for line in var_file_handle:
              parts = line.split("=")
              field_name = parts[0].strip()
              field_value = parts[1].strip()
              if field_name in ["model_left_context", "left_context"]:
                  variables["model_left_context"] = int(field_value)
              elif field_name in [
                  "model_right_context",
                  "right_context",
              ]:
                  variables["model_right_context"] = int(field_value)
              elif field_name == "num_hidden_layers":
                  if int(field_value) > 1:
                      raise Exception(
                          "You have num_hidden_layers={0} (real meaning: your config files "
                          "are intended to do discriminative pretraining).  Since Kaldi 5.2, "
                          "this is no longer supported --> use newer config-creation scripts, "
                          "i.e. xconfig_to_configs.py.".format(
                              field_value
                          )
                      )
              else:
                  variables[field_name] = field_value
  
          return variables
      except ValueError:
          # we will throw an error at the end of the function so I will just pass
          pass
  
      raise Exception(
          "Error while parsing the file {0}".format(var_file)
      )
  
imports: from __future__ import division
  import argparse
  import glob
  import logging
  import os
  import math
  import re
  import shutil
  import libs.common
  from libs.nnet3.train.dropout_schedule import *
  import unittest
label_desc: Use image node_id 13 for calling a global function with example usage: parse_generic_config_vars_file(var_file) and returns: variables