class_docstr: Wrapper for a token-based input transformer.
  
  func should accept a list of tokens (5-tuples, see tokenize docs), and
  return an iterable which can be passed to tokenize.untokenize().
class_docstr_tok: ["Wrapper","for","a","token-based","input","transformer",".","func","should","accept","a","list","of","tokens","(","5-tuples",",","see","tokenize","docs",")",",","and","return","an","iterable","which","can","be","passed","to","tokenize.untokenize","(",")","."]
code: def __init__(self, func):
      self.func = func
      self.buf = []
      self.reset_tokenizer()
  
imports: import abc
  import functools
  import re
  from IPython.core.splitinput import LineInfo
  from IPython.utils import tokenize2
  from IPython.utils.openpy import cookie_comment_re
  from IPython.utils.py3compat import with_metaclass, PY3
  from IPython.utils.tokenize2 import generate_tokens, untokenize, TokenError
label_desc: Use image node_id 1 to create a new TokenInputTransformer object from inherited base classes: InputTransformer with example: obj = TokenInputTransformer(func)