code: def _compute_class_counts(self, encoded_dataset):
      counter = Counter()
      for doc, target_arr in encoded_dataset:
          target_arr = np.asarray(target_arr)
          decoded_targets = self.label_encoder.inverse_transform(
              target_arr
          )
          if self.multi_label:
              for label in decoded_targets:
                  counter.update(label)
          else:
              counter.update(decoded_targets)
      return counter
  
imports: import copy
  import os
  from collections import Counter, defaultdict
  import math
  from typing import Dict, List, Tuple, Union
  import tensorflow
  import numpy
  from finetune.base import BaseModel
  from finetune.encoding.target_encoders import SequenceLabelingEncoder, SequenceMultiLabelingEncoder
  from finetune.nn.target_blocks import sequence_labeler
  from finetune.nn.crf import sequence_decode
  from finetune.encoding.sequence_encoder import finetune_to_indico_sequence
  from finetune.encoding.input_encoder import get_spacy
  from finetune.input_pipeline import BasePipeline
  from finetune.util.metrics import sequences_overlap
  from finetune.encoding.input_encoder import tokenize_context
label_desc: Use image node_id 5 for calling the SequencePipeline obj's underlying member method code with example usage: obj._compute_class_counts(encoded_dataset) and returns: counter