code: def calculate_metric(self) -> PRF1Metrics:
      all_confusions = AllConfusions()
      for pred, expect in zip(self.all_preds, self.all_targets):
          pred_seq, expect_seq = [], []
          for p, e in zip(pred, expect):
              if e != self.pad_idx:
                  pred_seq.append(self.label_names[p])
                  expect_seq.append(self.label_names[e])
          expect_spans = convert_bio_to_spans(expect_seq)
          pred_spans = convert_bio_to_spans(pred_seq)
  
          expect_spans_set = set(expect_spans)
          pred_spans_set = set(pred_spans)
  
          true_positive = expect_spans_set & pred_spans_set
          false_positive = pred_spans_set - expect_spans_set
          false_negative = expect_spans_set - pred_spans_set
          all_confusions.confusions += Confusions(
              TP=len(true_positive),
              FP=len(false_positive),
              FN=len(false_negative),
          )
          for span in true_positive:
              all_confusions.per_label_confusions.update(
                  span.label, "TP", 1
              )
          for span in false_positive:
              all_confusions.per_label_confusions.update(
                  span.label, "FP", 1
              )
          for span in false_negative:
              all_confusions.per_label_confusions.update(
                  span.label, "FN", 1
              )
  
      return all_confusions.compute_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 3 for calling the NERMetricReporter obj's underlying member method code with example usage: obj.calculate_metric() and returns: all_confusions