class_docstr: Tensorizer for Cross-lingual LM tasks. Works for single sentence as well
  as sentence pair.
class_docstr_tok: ["Tensorizer","for","Cross-lingual","LM","tasks",".","Works","for","single","sentence","as","well","as","sentence","pair","."]
code: def numberize(self, row: Dict) -> Tuple[Any, ...]:
      per_sentence_tokens = [
          self.tokenizer.tokenize(row[column])
          for column in self.columns
      ]
      per_sentence_languages = [
          self.get_lang_id(row, self.language_column)
      ] * len(self.columns)
      return self.tensorizer_script_impl.numberize(
          per_sentence_tokens, per_sentence_languages
      )
  
imports: from typing import Any, Dict, List, Optional, Tuple
  import torch
  from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary
  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 Tokenizer
  from pytext.data.utils import Vocabulary
  from pytext.data.xlm_constants import LANG2ID_15
  from pytext.torchscript.utils import ScriptBatchInput
  from pytext.torchscript.vocab import ScriptVocabulary
  from pytext.utils.file_io import PathManager
  from pytext.utils.lazy import lazy_property
label_desc: Use image node_id 6 for calling the XLMTensorizer obj's underlying member method code with example usage: obj.numberize(row) and returns: self