class_docstr: Multi choice question finetune model.
  
  :param config: A :py:class:`finetune.config.Settings` object or None (for default config).
docstr: Produces a list of most likely class labels as determined by the fine-tuned model.
  
  
  :param question: List or array of text, shape [batch]
  :param answers: List or array of text, shape [batch, n_answers]
  :returns: list of class labels.
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code: def predict(self, questions, answers, context=None, **kwargs):
      """
      Produces a list of most likely class labels as determined by the fine-tuned model.
  
  
      :param question: List or array of text, shape [batch]
      :param answers: List or array of text, shape [batch, n_answers]
      :returns: list of class labels.
      """
      zipped_data = self.input_pipeline.zip_list_to_dict(
          X=list(zip(questions, answers)), context=context
      )
      raw_preds = self._inference(
          zipped_data,
          predict_keys=[PredictMode.NORMAL],
          context=context,
          **kwargs,
      )
      raw_ids = self.input_pipeline.label_encoder.inverse_transform(
          np.asarray(raw_preds)
      )
      return [ans[i] for ans, i in zip(answers, raw_ids)]
  
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 8 for calling the MultipleChoice obj's underlying member method code with example usage: obj.predict(questions, answers, context) and returns: unknown