research/improve_nas/trainer/cifar10.py [135:151]:
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      dataset = dataset.map(
          functools.partial(
              self._preprocess_data, training=training, preprocess=preprocess))
      if training:
        dataset = dataset.apply(
            tf.contrib.data.shuffle_and_repeat(
                buffer_size=500, seed=self._seed))
      return dataset.batch(
          batch_size_,
          drop_remainder=use_tpu).prefetch(tf.data.experimental.AUTOTUNE
                                          ).make_one_shot_iterator().get_next()

    return input_fn

  def get_head(self, name=None):
    """Returns a `Head` instance for multiclass CIFAR-10 with the given name."""
    return tf.contrib.estimator.multi_class_head(
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research/improve_nas/trainer/cifar100.py [139:155]:
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      dataset = dataset.map(
          functools.partial(
              self._preprocess_data, training=training, preprocess=preprocess))
      if training:
        dataset = dataset.apply(
            tf.contrib.data.shuffle_and_repeat(
                buffer_size=500, seed=self._seed))
      return dataset.batch(
          batch_size_,
          drop_remainder=use_tpu).prefetch(tf.data.experimental.AUTOTUNE
                                          ).make_one_shot_iterator().get_next()

    return input_fn

  def get_head(self, name=None):
    """Returns a `Head` instance for CIFAR-100 with the given name."""
    return tf.contrib.estimator.multi_class_head(
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