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 column_schema(self):
      schema = super().column_schema
      if self.has_language_in_data:
          schema += [(self.language_column, str)]
      return schema
  
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 3 for calling the XLMTensorizer obj's underlying member method code with example usage: obj.column_schema() and returns: schema