code: def __init__(
      self,
      columns: List[str] = Config.columns,
      vocab: Vocabulary = None,
      tokenizer: Tokenizer = None,
      max_seq_len: int = Config.max_seq_len,
      **kwargs,
  ) -> None:
      super().__init__(
          columns=columns,
          vocab=vocab,
          tokenizer=tokenizer,
          max_seq_len=max_seq_len,
          **kwargs,
      )
  
imports: import itertools
  from typing import Any, Dict, List, Tuple
  import torch
  from pytext import resources
  from pytext.common.constants import SpecialTokens
  from pytext.config.component import ComponentType, create_component
  from pytext.data.bert_tensorizer import BERTTensorizerBase, BERTTensorizerBaseScriptImpl, build_fairseq_vocab
  from pytext.data.tokenizers import GPT2BPETokenizer, Tokenizer
  from pytext.data.utils import pad_and_tensorize, VocabBuilder, Vocabulary
  from pytext.torchscript.tensorizer import ScriptRoBERTaTensorizer
  from pytext.torchscript.vocab import ScriptVocabulary
  from pytext.utils.file_io import PathManager
label_desc: Use image node_id 2 to create a new RoBERTaTensorizer object from inherited base classes: BERTTensorizerBase with example: obj = RoBERTaTensorizer(columns, vocab, tokenizer, max_seq_len)