code: def calculate_mean_edit_distance_and_loss(iterator, dropout, reuse):
      r"""
      This routine beam search decodes a mini-batch and calculates the loss and mean edit distance.
      Next to total and average loss it returns the mean edit distance,
      the decoded result and the batch's original Y.
      """
      # Obtain the next batch of data
      (
          batch_filenames,
          (batch_x, batch_seq_len),
          batch_y,
      ) = iterator.get_next()
  
      if FLAGS.train_cudnn:
          rnn_impl = rnn_impl_cudnn_rnn
      else:
          rnn_impl = rnn_impl_lstmblockfusedcell
  
      # Calculate the logits of the batch
      logits, _ = create_model(
          batch_x,
          batch_seq_len,
          dropout,
          reuse=reuse,
          rnn_impl=rnn_impl,
      )
  
      # Compute the CTC loss using TensorFlow's `ctc_loss`
      total_loss = tfv1.nn.ctc_loss(
          labels=batch_y, inputs=logits, sequence_length=batch_seq_len
      )
  
      # Check if any files lead to non finite loss
      non_finite_files = tf.gather(
          batch_filenames, tfv1.where(~tf.math.is_finite(total_loss))
      )
  
      # Calculate the average loss across the batch
      avg_loss = tf.reduce_mean(input_tensor=total_loss)
  
      # Finally we return the average loss
      return avg_loss, non_finite_files
  
imports: from __future__ import absolute_import, division, print_function
  import os
  import sys
  import absl.app
  import numpy
  import progressbar
  import shutil
  import tensorflow
  import tensorflow.compat.v1
  import time
  from datetime import datetime
  from ds_ctcdecoder import ctc_beam_search_decoder, Scorer
  from .evaluate import evaluate
  from six.moves import zip, range
  from .util.config import Config, initialize_globals
  from .util.checkpoints import load_or_init_graph_for_training, load_graph_for_evaluation, reload_best_checkpoint
  from .util.evaluate_tools import save_samples_json
  from .util.feeding import create_dataset, audio_to_features, audiofile_to_features
  from .util.flags import create_flags, FLAGS
  from .util.helpers import check_ctcdecoder_version, ExceptionBox
  from .util.logging import create_progressbar, log_debug, log_error, log_info, log_progress, log_warn
  from .util.io import open_remote, remove_remote, listdir_remote, is_remote_path, isdir_remote
label_desc: Use image node_id 8 for calling a global function with example usage: calculate_mean_edit_distance_and_loss(iterator, dropout, reuse) and returns: avg_loss,  non_finite_files