code: def get_learning_rate(
      iter,
      num_jobs,
      num_iters,
      num_archives_processed,
      num_archives_to_process,
      initial_effective_lrate,
      final_effective_lrate,
  ):
      if iter + 1 >= num_iters:
          effective_learning_rate = final_effective_lrate
      else:
          effective_learning_rate = initial_effective_lrate * math.exp(
              num_archives_processed
              * math.log(
                  float(final_effective_lrate) / initial_effective_lrate
              )
              / num_archives_to_process
          )
  
      return num_jobs * effective_learning_rate
  
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 21 for calling a global function with example usage: get_learning_rate(iter, num_jobs, num_iters, num_archives_processed, num_archives_to_process, initial_effective_lrate, final_effective_lrate) and returns: unknown