code: def reduce_metrics(self, logging_outputs, criterion):
      super().reduce_metrics(logging_outputs, criterion)
  
      zero = torch.scalar_tensor(0.0)
      num_char_errors = sum(
          log.get("_num_char_errors", zero) for log in logging_outputs
      )
      num_chars = sum(
          log.get("_num_chars", zero) for log in logging_outputs
      )
      num_word_errors = sum(
          log.get("_num_word_errors", zero) for log in logging_outputs
      )
      num_words = sum(
          log.get("_num_words", zero) for log in logging_outputs
      )
      num_pred_chars = sum(
          log.get("_num_pred_chars", zero) for log in logging_outputs
      )
  
      lm_score_sum = sum(
          log.get("_lm_score_sum", zero) for log in logging_outputs
      )
      vocab_seen = (
          sum(log.get("_vocab_seen", zero) for log in logging_outputs)
          .bool()
          .sum()
          .item()
      )
      kaldi_score_sum = sum(
          log.get("_kaldi_score_sum", zero) for log in logging_outputs
      )
      word_lm_sum = sum(
          log.get("_word_lm_sum", zero) for log in logging_outputs
      )
  
      metrics.log_scalar_sum("_num_char_errors", num_char_errors)
      metrics.log_scalar_sum("_num_chars", num_chars)
      metrics.log_scalar_sum("_num_word_errors", num_word_errors)
      metrics.log_scalar_sum("_num_words", num_words)
  
      metrics.log_scalar_sum("lm_score_sum", lm_score_sum)
      metrics.log_scalar_sum("num_pred_chars", num_pred_chars)
  
      if self.cfg.word_kenlm_path is not None:
          metrics.log_scalar_sum("kaldi_score_sum", kaldi_score_sum)
          metrics.log_scalar_sum("word_lm_sum", word_lm_sum)
  
      if num_chars > 0:
          metrics.log_derived(
              "uer",
              lambda meters: meters["_num_char_errors"].sum
              * 100.0
              / meters["_num_chars"].sum
              if meters["_num_chars"].sum > 0
              else float("nan"),
          )
  
          if lm_score_sum < 0 and vocab_seen > 0:
              metrics.log_scalar(
                  "vocab_seen_pct", vocab_seen / self.num_symbols
              )
  
              metrics.log_derived(
                  "weighted_lm_ppl",
                  lambda meters: math.pow(
                      10,
                      -meters["lm_score_sum"].sum
                      / (
                          meters["num_pred_chars"].sum
                          + meters["nsentences"].sum
                      ),  # account for </s>
                  )
                  / meters["vocab_seen_pct"].avg
                  ** self.cfg.vocab_usage_power,
              )
  
              metrics.log_derived(
                  "lm_ppl",
                  lambda meters: math.pow(
                      10,
                      -meters["lm_score_sum"].sum
                      / (
                          meters["num_pred_chars"].sum
                          + meters["nsentences"].sum
                      ),  # account for </s>
                  ),
              )
          else:
              metrics.log_derived(
                  "weighted_lm_ppl", lambda meters: float("inf")
              )
  
      if num_words > 0:
          if word_lm_sum != 0:
              metrics.log_derived(
                  "word_lm_ppl",
                  lambda meters: math.pow(
                      10,
                      -meters["word_lm_sum"].sum
                      / (
                          meters["_num_words"].sum
                          + meters["nsentences"].sum
                      ),  # account for </s>
                  ),
              )
              metrics.log_derived(
                  "weighted_word_lm_ppl",
                  lambda meters: math.pow(
                      10,
                      -meters["word_lm_sum"].sum
                      / (
                          meters["_num_words"].sum
                          + meters["nsentences"].sum
                      ),  # account for </s>
                  )
                  / meters["vocab_seen_pct"].avg
                  ** self.cfg.vocab_usage_power,
              )
  
          if self.cfg.word_kenlm_path is not None:
              metrics.log_derived(
                  "kaldi_score",
                  lambda meters: meters["kaldi_score_sum"].sum
                  / meters["nsentences"].sum,
              )
  
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 9 for calling the UnpairedAudioText obj's underlying member method code with example usage: obj.reduce_metrics(logging_outputs, criterion) without return types