class_docstr: Multi choice question finetune model.
  
  :param config: A :py:class:`finetune.config.Settings` object or None (for default config).
code: def _pre_target_model_hook(self, featurizer_state):
      if "context" in featurizer_state:
          context_embed = featurizer_state["context"]
          featurizer_state["features"] = tf.concat(
              (
                  featurizer_state["features"],
                  tf.reduce_mean(input_tensor=context_embed, axis=2),
              ),
              -1,
          )
  
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 MultipleChoice obj's underlying member method code with example usage: obj._pre_target_model_hook(featurizer_state) without return types