class_docstr: Labels each token in a sequence as belonging to 1 of N token classes.
  
  :param config: A :py:class:`finetune.config.Settings` object or None (for default config).
  :param \**kwargs: key-value pairs of config items to override.
class_docstr_tok: ["Labels","each","token","in","a","sequence","as","belonging","to","1","of","N","token","classes",".",":","param","config",":","A",":","py",":","class",":","`","finetune.config.Settings","`","object","or","None","(","for","default","config",")",".",":","param","\\","*","*","kwargs",":","key-value","pairs","of","config","items","to","override","."]
code: def _initialize(self):
      self.multi_label = self.config.multi_label_sequences
      return super()._initialize()
  
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 3 for calling the SequenceLabeler obj's underlying member method code with example usage: obj._initialize() and returns: super