code: def run_eval(model, data_module):
      total_edit_distance = 0
      total_length = 0
      dataloader = data_module.test_dataloader()
      with torch.no_grad():
          for idx, (batch, sample) in enumerate(dataloader):
              actual = sample[0][-1]
              predicted = model(batch)
              total_edit_distance += compute_word_level_distance(
                  actual, predicted
              )
              total_length += len(actual.split())
              if idx % 100 == 0:
                  logger.warning(
                      f"Processed elem {idx}; WER: {total_edit_distance / total_length}"
                  )
      logger.warning(f"Final WER: {total_edit_distance / total_length}")
      return total_edit_distance / total_length
  
file_path: audio/examples/avsr/eval.py
imports: import logging
  from argparse import ArgumentParser
  import sentencepiece
  import torch
  import torchaudio
  from transforms import get_data_module
label_desc: Use image node_id 3 for calling a global function with example usage: run_eval(model, data_module) and returns: unknown