code: def valid_step(self, sample, model, criterion):
      loss, sample_size, logging_output = super().valid_step(
          sample, model, criterion
      )
      if self.cfg.eval_wer_parse and self.cfg.autoregressive:
          metrics = self._inference_with_wer_parse(
              self.sequence_generator, sample, model
          )
          logging_output["_num_char_errors"] = metrics[
              "num_char_errors"
          ]
          logging_output["_num_chars"] = metrics["num_chars"]
          logging_output["_num_word_errors"] = metrics[
              "num_word_errors"
          ]
          logging_output["_num_words"] = metrics["num_words"]
          logging_output["_num_em_errors"] = metrics["num_em_errors"]
          logging_output["_num_ems"] = metrics["num_ems"]
          logging_output["_num_tree_errors"] = metrics[
              "num_tree_errors"
          ]
          logging_output["_num_trees"] = metrics["num_trees"]
      if self.cfg.eval_wer and self.cfg.autoregressive:
          metrics = self._inference_with_wer(
              self.sequence_generator, sample, model
          )
          logging_output["_num_char_errors"] = metrics[
              "num_char_errors"
          ]
          logging_output["_num_chars"] = metrics["num_chars"]
          logging_output["_num_word_errors"] = metrics[
              "num_word_errors"
          ]
          logging_output["_num_words"] = metrics["num_words"]
      if self.cfg.eval_bleu and self.cfg.autoregressive:
          metrics = self._inference_with_bleu(
              self.sequence_generator, sample, model
          )
          logging_output["_bleu_sys_len"] = metrics.sys_len
          logging_output["_bleu_ref_len"] = metrics.ref_len
          # we split counts into separate entries so that they can be
          # summed efficiently across workers using fast-stat-sync
          assert len(metrics.counts) == 4
          for i in range(4):
              logging_output[f"_bleu_counts_{i}"] = metrics.counts[i]
              logging_output[f"_bleu_totals_{i}"] = metrics.totals[i]
      return loss, sample_size, logging_output
  
imports: import logging
  import os
  import torch
  import json
  from argparse import Namespace
  from dataclasses import dataclass, field
  from typing import Optional, Any
  from fairseq.data import AddTargetDataset, Dictionary, encoders
  from fairseq.tasks.audio_pretraining import AudioPretrainingTask, AudioPretrainingConfig
  from fairseq.dataclass import FairseqDataclass
  from fairseq.dataclass.configs import GenerationConfig
  from fairseq.data.text_compressor import TextCompressor, TextCompressionLevel
  from .None import register_task
  from ..None import utils
  from ..logging import metrics
label_desc: Use image node_id 5 for calling the NLUFinetuningTask obj's underlying member method code with example usage: obj.valid_step(sample, model, criterion) and returns: loss,  sample_size,  logging_output