class_docstr: Tensorizer for token level classification tasks such as NER, POS etc
  using RoBERTa. Here each token has an associated label and the tensorizer
  should output a label tensor as well. The input for this tensorizer comes
  from the CoNLLUNERDataSource data source.
code: def torchscriptify(self):
      return ScriptRoBERTaTensorizer(
          tokenizer=self.tokenizer.torchscriptify(),
          vocab=ScriptVocabulary(
              list(self.vocab),
              pad_idx=self.vocab.get_pad_index(),
              bos_idx=self.vocab.get_bos_index(),
              eos_idx=self.vocab.get_eos_index(),
          ),
          max_seq_len=self.max_seq_len,
      )
  
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 7 for calling the RoBERTaTokenLevelTensorizer obj's underlying member method code with example usage: obj.torchscriptify() and returns: ScriptRoBERTaTensorizer