code: def multi_choice_question(
      hidden,
      targets,
      n_targets,
      config,
      train=False,
      reuse=None,
      **kwargs,
  ):
      with tf.compat.v1.variable_scope("model", reuse=reuse):
          if targets is not None:
              targets = tf.cast(targets, tf.int32)
          hidden = dropout(hidden, config.clf_p_drop, train)
          hidden = tf.unstack(hidden, num=n_targets, axis=1)
          hidden = tf.concat(hidden, axis=0)
  
          clf_out = perceptron(hidden, 1, config)
          clf_out = tf.split(clf_out, n_targets, axis=0)
          clf_out = tf.concat(clf_out, 1)
  
          if targets is None:
              clf_losses = None
          else:
              clf_losses = (
                  tf.nn.sparse_softmax_cross_entropy_with_logits(
                      logits=clf_out, labels=tf.stop_gradient(targets)
                  )
              )
  
              clf_losses = _apply_class_weight(
                  clf_losses,
                  targets,
                  kwargs.get("class_weights"),
                  norm_grads=config.renorm_after_class_weights,
              )
  
          return {"logits": clf_out, "losses": clf_losses}
  
imports: import functools
  import tensorflow
  from tensorflow_addons.text.crf import crf_log_likelihood
  from finetune.base_models.gpt.featurizer import attn, dropout, norm
  from finetune.util.shapes import shape_list, merge_leading_dims
  from finetune.optimizers.recompute_grads import recompute_grad
  from finetune.errors import FinetuneError
  from finetune.nn.activations import act_fns
  from finetune.nn.nn_utils import norm
  from tensorflow.python.framework import function
label_desc: Use image node_id 7 for calling a global function with example usage: multi_choice_question(hidden, targets, n_targets, config, train, reuse) and returns: dict