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 from_config(cls, config: Config):
      tokenizer = create_component(
          ComponentType.TOKENIZER, config.tokenizer
      )
      with PathManager.open(config.vocab_file) as f:
          vocab = build_fairseq_vocab(
              vocab_file=f,
              special_token_replacements={
                  "<pad>": SpecialTokens.PAD,
                  "<s>": SpecialTokens.BOS,
                  "</s>": SpecialTokens.EOS,
                  "<unk>": SpecialTokens.UNK,
                  "<mask>": SpecialTokens.MASK,
              },
          )
      return cls(
          columns=config.columns,
          vocab=vocab,
          tokenizer=tokenizer,
          max_seq_len=config.max_seq_len,
          labels_columns=config.labels_columns,
          labels=config.labels,
      )
  
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 1 for calling the RoBERTaTokenLevelTensorizer obj's underlying member method code with example usage: obj.from_config(cls, config) and returns: cls