code: def calculate_metric(self):
      return compute_classification_metrics(
          list(
              itertools.chain.from_iterable(
                  (
                      LabelPrediction(s, p, e)
                      for s, p, e in zip(scores, pred, expect)
                      if e != self.pad_idx
                  )
                  for scores, pred, expect in zip(
                      self.all_scores, self.all_preds, self.all_targets
                  )
              )
          ),
          self.label_names,
          self.calculate_loss(),
      )
  
imports: import itertools
  import re
  from collections import Counter
  from typing import Dict, List, NamedTuple
  from pytext.common.constants import DatasetFieldName, SpecialTokens, Stage
  from pytext.data import CommonMetadata
  from pytext.metrics import AllConfusions, compute_classification_metrics, compute_multi_label_multi_class_soft_metrics, Confusions, LabelPrediction, PRF1Metrics
  from pytext.metrics.intent_slot_metrics import compute_prf1_metrics, Node, NodesPredictionPair, Span
  from pytext.utils.data import merge_token_labels_to_slot, parse_slot_string
  from .channel import Channel, ConsoleChannel, FileChannel
  from .metric_reporter import MetricReporter
label_desc: Use image node_id 3 for calling the SequenceTaggingMetricReporter obj's underlying member method code with example usage: obj.calculate_metric() and returns: compute_classification_metrics