code: def aggregate_topkpreds(self, batch_scores, batch_context=None):
      # first process the batch_scores so that it only contains the top k choices
      values, indices = torch.topk(batch_scores, self.topk)
      self.aggregate_data(self.all_topk_preds, indices)
  
imports: from enum import Enum
  from itertools import tee, zip_longest
  from typing import Generator, List, Optional, Sequence
  import torch
  from pytext.common.constants import Stage
  from pytext.data import CommonMetadata
  from pytext.metrics import ClassificationMetrics, compute_classification_metrics, compute_multi_label_classification_metrics, LabelListPrediction, LabelPrediction, LabelTopKPrediction, RECALL_AT_PRECISION_THRESHOLDS, safe_division
  from pytext.metrics.intent_slot_metrics import PerLabelConfusions
  from .channel import Channel, ConsoleChannel, FileChannel
  from .metric_reporter import MetricReporter
label_desc: Use image node_id 5 for calling the TopKClassificationMetricReporter obj's underlying member method code with example usage: obj.aggregate_topkpreds(batch_scores, batch_context) without return types