code: def calculate_loss(self):
      total_loss = n_words = pos = 0
      for loss, batch_size in zip(self.all_loss, self.batch_size):
          num_words_in_batch = sum(
              self.all_context["seq_lens"][pos : pos + batch_size]
          )
          pos = pos + batch_size
          total_loss += loss * num_words_in_batch
          n_words += num_words_in_batch
      return total_loss / float(n_words)
  
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 WordTaggingMetricReporter obj's underlying member method code with example usage: obj.calculate_loss() and returns: unknown