code: def calculate_metric(self):
      # If we are running in memory efficient mode, then scores in
      # LabelPrediction should be an empty list
      label_predictions = [
          LabelPrediction(scores, pred, expect)
          for scores, pred, expect in zip_longest(
              self.all_scores,
              self.all_preds,
              self.all_targets,
              fillvalue=[],
          )
      ]
      return compute_classification_metrics(
          label_predictions,
          self.label_names,
          self.calculate_loss(),
          # Compute soft-metrics only if self.is_memory_efficient is False
          average_precisions=(not self.is_memory_efficient),
          recall_at_precision_thresholds=self.recall_at_precision_thresholds,
      )
  
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 7 for calling the ClassificationMetricReporter obj's underlying member method code with example usage: obj.calculate_metric() and returns: compute_classification_metrics