class_docstr: Token object which is output by the tokenization
  
  Attributes:
      value (str): Tokenized string
      start (int): Start position of the token within the sentence
      end (int): End position of the token within the sentence
      normalized_value (str): Normalized value of the tokenized string
      stemmed_value (str): Stemmed value of the tokenized string
class_docstr_tok: ["Token","object","which","is","output","by","the","tokenization","Attributes",":","value","(","str",")",":","Tokenized","string","start","(","int",")",":","Start","position","of","the","token","within","the","sentence","end","(","int",")",":","End","position","of","the","token","within","the","sentence","normalized_value","(","str",")",":","Normalized","value","of","the","tokenized","string","stemmed_value","(","str",")",":","Stemmed","value","of","the","tokenized","string"]
code: def __eq__(self, other):
      if not isinstance(other, type(self)):
          return False
      return (
          self.value == other.value
          and self.start == other.start
          and self.end == other.end
      )
  
file_path: snips-nlu/snips_nlu/preprocessing.py
imports: from __future__ import unicode_literals
  from builtins import object
  from snips_nlu.resources import get_stems
label_desc: Use image node_id 2 for calling the Token obj's underlying member method code with example usage: obj.__eq__(other) and returns: unknown, False