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 tensorizer_script_impl(self):
      languages = [0] * (max(list(self.lang2id.values())) + 1)
      for k, v in self.lang2id.items():
          languages[v] = k
      return self.__TENSORIZER_SCRIPT_IMPL__(
          tokenizer=self.tokenizer,
          vocab=self.vocab,
          language_vocab=languages,
          max_seq_len=self.max_seq_len,
          default_language=self.default_language,
      )
  
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 4 for calling the XLMTensorizer obj's underlying member method code with example usage: obj.tensorizer_script_impl() and returns: self