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/image_classification.py
imports: import os.path
  import logging
  from dataclasses import dataclass
  import torch
  from torchvision import transforms
  from fairseq.dataclass import FairseqDataclass
  from fairseq.tasks import register_task
  from fairseq.logging import metrics
  from .image_pretraining import ImagePretrainingConfig, ImagePretrainingTask, IMG_EXTENSIONS
label_desc: Use image node_id 4 for calling the ImageClassificationTask obj's underlying member method code with example usage: obj.reduce_metrics(logging_outputs, criterion) without return types