code: def valid_step(self, sample, model, criterion):
      res = model(
          **sample["net_input"],
          dense_x_only=True,
      )
  
      dense_x = res["logits"]
      padding_mask = res["padding_mask"]
  
      word_scores = None
      if self.compute_word_score is not None:
          word_scores = self.compute_word_score(
              dense_x.cpu(), padding_mask.cpu()
          )
  
      z = dense_x.argmax(-1)
      z[padding_mask] = self.target_dictionary.pad()
  
      vocab_seen = torch.zeros(self.num_symbols, dtype=torch.bool)
  
      import editdistance
  
      c_err = 0
      c_len = 0
      pred_c_len = 0
      lm_score_sum = 0
      for i, (x, t, id) in enumerate(
          zip(
              z,
              sample["target"]
              if "target" in sample
              else [None] * len(z),
              sample["id"],
          )
      ):
          if t is not None:
              t = t[(t >= self.target_dictionary.nspecial)]
          x = x[
              (x >= self.target_dictionary.nspecial)
              & (
                  x
                  < (self.num_symbols + self.target_dictionary.nspecial)
              )
          ]
          if self.sil_id >= 0:
              x = x[x != self.sil_id]
  
          vocab_seen[x - self.target_dictionary.nspecial] = True
  
          pred_units_arr = x
          if self.cfg.ctc_eval:
              pred_units_arr = pred_units_arr.unique_consecutive()
              pred_units_arr = pred_units_arr[pred_units_arr != 0]
  
          if id == 0:
              if t is not None:
                  logger.info(
                      f"REF: {self.target_dictionary.string(t)}"
                  )
              logger.info(
                  f"HYP: {self.target_dictionary.string(pred_units_arr)}"
              )
  
              if self.kenlm is not None:
                  if t is not None:
                      ref_lm_s = self.compute_lm_score(
                          self.target_dictionary.string(t)
                      )
                      logger.info(
                          f"LM [REF]: {ref_lm_s}, {math.pow(10, -ref_lm_s / (len(t) + 1))}"
                      )
  
                  hyp_lm_s = self.compute_lm_score(
                      self.target_dictionary.string(pred_units_arr)
                  )
                  logger.info(
                      f"LM [HYP]: {hyp_lm_s}, {math.pow(10, -hyp_lm_s / (len(pred_units_arr) + 1))}"
                  )
  
          pred_units_arr = pred_units_arr.tolist()
  
          pred_c_len += len(pred_units_arr)
  
          if t is not None:
              t = t.tolist()
              c_err += editdistance.eval(pred_units_arr, t)
              c_len += len(t)
          else:
              c_len = pred_c_len
  
          if self.kenlm is not None:
              pred_str = self.target_dictionary.string(pred_units_arr)
              lm_score = self.compute_lm_score(pred_str)
              lm_score_sum += lm_score
  
      kaldi_score_sum = 0
      word_lm_sum = 0
      num_words = 0
      if word_scores is not None:
          for score, words in word_scores:
              kaldi_score_sum += score
              num_words += len(words)
              if self.word_kenlm is not None:
                  word_lm_sum += self.kenlm.score(" ".join(words))
  
      try:
          world_size = get_data_parallel_world_size()
      except:
          world_size = 1
  
      logging_output = {
          "loss": c_err,
          "_num_char_errors": c_err,
          "_num_chars": c_len,
          "_num_pred_chars": pred_c_len,
          "ntokens": c_len,
          "nsentences": z.size(0),
          "sample_size": c_len,
          "_world_size": world_size,
          "_lm_score_sum": lm_score_sum,
          "_kaldi_score_sum": kaldi_score_sum,
          "_word_lm_sum": word_lm_sum,
          "_num_words": num_words,
          "_vocab_seen": vocab_seen,
      }
  
      return c_err, c_len, logging_output
  
imports: from dataclasses import dataclass, field
  import logging
  import math
  import os
  from typing import Optional
  import torch
  from fairseq.logging import metrics
  from fairseq.tasks import FairseqTask, register_task
  from ..data import ExtractedFeaturesDataset, RandomInputDataset
  from fairseq.data import Dictionary, data_utils, StripTokenDataset
  from fairseq.dataclass import FairseqDataclass
  from fairseq.distributed.utils import get_data_parallel_world_size
  from omegaconf import MISSING
  from examples.speech_recognition.kaldi.kaldi_decoder import KaldiDecoder, KaldiDecoderConfig
label_desc: Use image node_id 4 for calling the UnpairedAudioText obj's underlying member method code with example usage: obj.valid_step(sample, model, criterion) and returns: c_err,  c_len,  logging_output