code: def reduce_metrics(self, logging_outputs, criterion):
      super().reduce_metrics(logging_outputs, criterion)
  
      if "correct" in logging_outputs[0]:
          zero = torch.scalar_tensor(0.0)
          correct = sum(
              log.get("correct", zero) for log in logging_outputs
          )
          metrics.log_scalar_sum("_correct", correct)
  
          metrics.log_derived(
              "accuracy",
              lambda meters: 100
              * meters["_correct"].sum
              / meters["sample_size"].sum,
          )
  
file_path: fairseq/examples/data2vec/tasks/mae_image_classification.py
imports: import logging
  import sys
  import torch
  from typing import Optional
  from dataclasses import dataclass, field
  from omegaconf import MISSING
  from fairseq.dataclass import FairseqDataclass
  from fairseq.tasks import FairseqTask, register_task
  from fairseq.logging import metrics
label_desc: Use image node_id 4 for calling the MaeImageClassificationTask obj's underlying member method code with example usage: obj.reduce_metrics(logging_outputs, criterion) without return types