class_docstr: Encapsulates the roberta tokenizer
class_docstr_tok: ["Encapsulates","the","roberta","tokenizer"]
code: def __init__(
      self,
      tokenizer,
      decoder=None,
      entity_type=None,
      tag_type=None,
      lower_case=False,
      name=None,
  ):
      self.tokenizer = tokenizer
      self.name = tokenizer.name_or_path if name is None else name
      if decoder is None:
          try:
              from text_unidecode import unidecode
          except:
              raise Exception(
                  "Please install the text_unicode package to preprocess documents. "
                  "If you'd like to bypass this step, pass a text preprocessing "
                  "(e.g., lambda x: x) function into the decode parameter of this class."
              )
          self.decoder = unidecode
      else:
          self.decoder = decoder
      self.entity_type = entity_type
      self.tag_type = tag_type
      self.lower_case = lower_case
  
file_path: scattertext/scattertext/tokenizers/transformers.py
imports: import abc
  from abc import ABC
  from scattertext.WhitespaceNLP import Tok, _get_pos_tag, Doc
label_desc: Use image node_id 1 to create a new TransformerTokenizerWrapper object from inherited base classes: ABC with example: obj = TransformerTokenizerWrapper(tokenizer, decoder, entity_type, tag_type, lower_case, name)