class_docstr: Multi choice question finetune model.
  
  :param config: A :py:class:`finetune.config.Settings` object or None (for default config).
docstr: Embeds inputs in learned feature space. Can be called before or after calling :meth:`finetune`.
  
  :param questions: List or array of text, shape [batch]
  :param answers: List or array of text, shape [n_answers, batch]
  :returns: np.array of features of shape (n_examples, embedding_size).
docstr_tok: ["Embeds","inputs","in","learned","feature","space",".","Can","be","called","before","or","after","calling",":","meth",":","`","finetune","`",".",":","param","questions",":","List","or","array","of","text",",","shape","[","batch","]",":","param","answers",":","List","or","array","of","text",",","shape","[","n_answers",",","batch","]",":","returns",":","np.array","of","features","of","shape","(","n_examples",",","embedding_size",")","."]
code: def featurize(self, questions, answers, **kwargs):
      """
      Embeds inputs in learned feature space. Can be called before or after calling :meth:`finetune`.
  
      :param questions: List or array of text, shape [batch]
      :param answers: List or array of text, shape [n_answers, batch]
      :returns: np.array of features of shape (n_examples, embedding_size).
      """
      return super().featurize(self, zip(questions, answers), **kwargs)
  
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 10 for calling the MultipleChoice obj's underlying member method code with example usage: obj.featurize(questions, answers) and returns: super