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 _build_label_vocab(self, labels: List[str]):
      labels_vocab_builder = VocabBuilder()
      labels_vocab_builder.use_pad = True
      labels_vocab_builder.use_unk = False
  
      labels_vocab_builder.add_all(labels)
      return labels_vocab_builder.make_vocab()
  
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 3 for calling the RoBERTaTokenLevelTensorizer obj's underlying member method code with example usage: obj._build_label_vocab(labels) and returns: labels_vocab_builder