code: def get_input_model_info(input_model):
      """This function returns a dictionary with keys "model_left_context" and
      "model_right_context" and values equal to the left/right model contexts
      for input_model.
      This function is useful when using the --trainer.input-model option
      instead of initializing the model using configs.
      """
      variables = {}
      try:
          out = common_lib.get_command_stdout(
              """nnet3-info {0} | """ """head -4 """.format(input_model)
          )
          # out looks like this
          # left-context: 7
          # right-context: 0
          # num-parameters: 90543902
          # modulus: 1
          for line in out.split("\n"):
              parts = line.split(":")
              if len(parts) != 2:
                  continue
              if parts[0].strip() == "left-context":
                  variables["model_left_context"] = int(
                      parts[1].strip()
                  )
              elif parts[0].strip() == "right-context":
                  variables["model_right_context"] = int(
                      parts[1].strip()
                  )
  
      except ValueError:
          pass
      return variables
  
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 14 for calling a global function with example usage: get_input_model_info(input_model) and returns: variables