class_docstr: Uses the T5 tokenizer to convert an input for processing.
  
  For more information, please see the T5 paper, "Exploring the Limits of
  Transfer Learning with a Unified Text-to-Text Transformer".
  Appendix D contains information about the various tasks supported
  by T5.
  
  Supports the following modes:
  
  * summarization: summarize English text
  * english_to_german: translate English to German
  * english_to_french: translate English to French
  * english_to_romanian: translate English to Romanian
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docstr: Args:
      text (:obj:`str`, :obj:`List[str]`):
              The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings.
docstr_tok: ["Args",":","text","(",":","obj",":","`","str","`",",",":","obj",":","`","List","[","str","]","`",")",":","The","sequence","or","batch","of","sequences","to","be","encoded",".","Each","sequence","can","be","a","string","or","a","list","of","strings","."]
code: def __call__(self, text, *args, **kwargs):
      """
      Args:
          text (:obj:`str`, :obj:`List[str]`):
                  The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings.
      """
      assert isinstance(text, str) or (
          isinstance(text, (list, tuple))
          and (len(text) == 0 or isinstance(text[0], str))
      ), "`text` must be a string or a list of strings."
      if isinstance(text, str):
          text = self.tokenization_prefix + text
      else:
          for i in range(len(text)):
              text[i] = self.tokenization_prefix + text[i]
      return self.tokenizer(text, *args, **kwargs)
  
imports: import transformers
label_desc: Use image node_id 2 for calling the T5Tokenizer obj's underlying member method code with example usage: obj.__call__(text) and returns: self