code: def calculate_metric(self):
      return compute_prf1_metrics(
          [
              NodesPredictionPair(
                  get_slots(
                      merge_token_labels_to_slot(
                          token_range,
                          self.process_pred(pred[0:seq_len]),
                          self.use_bio_labels,
                      )
                  ),
                  get_slots(slots_label),
              )
              for pred, seq_len, token_range, slots_label in zip(
                  self.all_preds,
                  self.all_context[DatasetFieldName.SEQ_LENS],
                  self.all_context[DatasetFieldName.TOKEN_RANGE],
                  self.all_context[DatasetFieldName.RAW_WORD_LABEL],
              )
          ]
      )[1]
  
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 5 for calling the WordTaggingMetricReporter obj's underlying member method code with example usage: obj.calculate_metric() and returns: compute_prf1_metrics