class_docstr: Multi choice question finetune model.
  
  :param config: A :py:class:`finetune.config.Settings` object or None (for default config).
docstr: Produces a probability distribution over classes for each example in X.
  
  
  :param question: List or array of text, shape [batch]
  :param answers: List or array of text, shape [batch, n_answers]
  :returns: list of dictionaries.  Each dictionary maps from a class label to its assigned class probability.
docstr_tok: ["Produces","a","probability","distribution","over","classes","for","each","example","in","X.",":","param","question",":","List","or","array","of","text",",","shape","[","batch","]",":","param","answers",":","List","or","array","of","text",",","shape","[","batch",",","n_answers","]",":","returns",":","list","of","dictionaries",".","Each","dictionary","maps","from","a","class","label","to","its","assigned","class","probability","."]
code: def predict_proba(self, questions, answers, context=None, **kwargs):
      """
      Produces a probability distribution over classes for each example in X.
  
  
      :param question: List or array of text, shape [batch]
      :param answers: List or array of text, shape [batch, n_answers]
      :returns: list of dictionaries.  Each dictionary maps from a class label to its assigned class probability.
      """
      answers = list_transpose(answers)
      zipped_data = self.input_pipeline.zip_list_to_dict(
          X=list(zip(questions, answers)), context=context
      )
      raw_probas = self._inference(
          zipped_data, predict_keys=[PredictMode.PROBAS], **kwargs
      )
      formatted_predictions = []
      for probas, *answers_per_sample in zip(raw_probas, *answers):
          formatted_predictions.append(
              dict(zip(answers_per_sample, probas))
          )
      return formatted_predictions
  
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 9 for calling the MultipleChoice obj's underlying member method code with example usage: obj.predict_proba(questions, answers, context) and returns: formatted_predictions