code: def calculate_metric(self):
      list_score_pred_expect = []
      for label_idx, _ in enumerate(self.label_names):
          list_score_pred_expect.append(
              list(
                  itertools.chain.from_iterable(
                      (
                          LabelPrediction(s, p, e)
                          for s, p, e in zip(
                              scores[label_idx],
                              pred[label_idx],
                              expect[label_idx],
                          )
                          if e != self.pad_idx[label_idx]
                      )
                      for scores, pred, expect in zip(
                          self.all_scores,
                          self.all_preds,
                          self.all_targets,
                      )
                  )
              )
          )
  
      metrics = compute_multi_label_multi_class_soft_metrics(
          list_score_pred_expect, self.label_names, self.label_vocabs
      )
      return metrics
  
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 7 for calling the MultiLabelSequenceTaggingMetricReporter obj's underlying member method code with example usage: obj.calculate_metric() and returns: metrics