class_docstr: Trainer for a classifier that always predicts the correct value.
code: def predict(self, unused_session, indices_unlabeled, **unused_kwargs):
      labels = self.data.get_original_labels(indices_unlabeled)
      num_samples = len(indices_unlabeled)
      predictions = np.zeros((num_samples, self.data.num_classes))
      predictions[np.arange(num_samples), labels] = 1.0
      return predictions
  
imports: import logging
  import os
  from .adversarial_dense import entropy_y_x
  from .adversarial_dense import get_loss_vat
  import numpy
  import tensorflow
  from .trainer_base import batch_iterator
  from .trainer_base import Trainer
label_desc: Use image node_id 3 for calling the TrainerPerfectClassification obj's underlying member method code with example usage: obj.predict(unused_session, indices_unlabeled) and returns: predictions