code: def text_to_tokens_mask(self, X, Y=None, context=None):
      out_gen = self._text_to_ids(X, pad_token=self.config.pad_token)
      for i, out in enumerate(out_gen):
          if context is None:
              feats = {"tokens": out.token_ids}
          else:
              num_answers = len(out.tokens)
              tokenized_context = []
              for answer_idx in range(num_answers):
                  out_instance = EncodedOutput(
                      token_ids=out.token_ids[answer_idx],
                      tokens=out.token_ids[answer_idx],
                      token_ends=out.token_ends,
                      token_starts=out.token_starts,
                  )
                  context_instance = (
                      context[0] + context[answer_idx + 1]
                  )
                  tokenized_context.append(
                      tokenize_context(
                          context_instance, out_instance, self.config
                      )
                  )
              feats = {
                  "tokens": out.token_ids,
                  "context": tokenized_context,
              }
          if Y is None:
              yield feats
          else:
              yield feats, self.label_encoder.transform([Y])[0]
  
imports: import numpy
  from finetune.base import BaseModel
  from finetune.input_pipeline import BasePipeline
  from finetune.encoding.input_encoder import EncodedOutput
  from finetune.encoding.target_encoders import IDEncoder
  import tensorflow
  from finetune.nn.target_blocks import multi_choice_question
  from finetune.util import list_transpose
  from finetune.encoding.input_encoder import tokenize_context
  from finetune.model import PredictMode
label_desc: Use image node_id 4 for calling the MultipleChoicePipeline obj's underlying member method code with example usage: obj.text_to_tokens_mask(X, Y, context) without return types