docstr: :param text: Text that contains tokens to be classified
  :return:
docstr_tok: [":","param","text",":","Text","that","contains","tokens","to","be","classified",":","return",":"]
code: def classify_token(
      self, text: str
  ) -> List[TokenClassificationResult]:
      """
      :param text: Text that contains tokens to be classified
      :return:
      """
  
      # loads pipeline if it hasn't been loaded already.
      self._load_pipeline()
  
      if not isinstance(text, str):
          raise ValueError(
              'the "text" argument must be a single string'
          )
      results = self._pipeline(text)
  
      return [
          TokenClassificationResult(
              word=answer["word"],
              score=answer["score"],
              entity=answer["entity"],
              index=answer["index"],
              start=answer["start"],
              end=answer["end"],
          )
          for answer in results
      ]
  
file_path: happy-transformer/happytransformer/happy_token_classification.py
imports: from dataclasses import dataclass
  from typing import List, Union
  from transformers import AutoModelForTokenClassification, TokenClassificationPipeline
  from happytransformer.adaptors import get_adaptor
  from happytransformer.happy_transformer import HappyTransformer
label_desc: Use image node_id 2 for calling the HappyTokenClassification obj's underlying member method code with example usage: obj.classify_token(text) and returns: unknown