human
Solve the following python programming problem given the following code:
# "hard sigmoid", useful for binary accuracy calculation from logits
def step(values):
    # negative values -> 0.0, positive values -> 1.0
    return 0.5 * (1.0 + tf.sign(values))

# augments images with a probability that is dynamically updated during training
class AdaptiveAugmenter(keras.Model):
    def __init__(self):
        super().__init__()

        # stores the current probability of an image being augmented
        self.probability = tf.Variable(0.0)

        # the corresponding augmentation names from the paper are shown above each layer
        # the authors show (see figure 4), that the blitting and geometric augmentations
        # are the most helpful in the low-data regime
        self.augmenter = keras.Sequential(
            [
                layers.InputLayer(input_shape=(image_size, image_size, 3)),
                # blitting/x-flip:
                layers.RandomFlip("horizontal"),
                # blitting/integer translation:
                layers.RandomTranslation(
                    height_factor=max_translation,
                    width_factor=max_translation,
                    interpolation="nearest",
                ),
                # geometric/rotation:
                layers.RandomRotation(factor=max_rotation),
                # geometric/isotropic and anisotropic scaling:
                layers.RandomZoom(
                    height_factor=(-max_zoom, 0.0), width_factor=(-max_zoom, 0.0)
                ),
            ],
            name="adaptive_augmenter",
        )

    def call(self, images, training):
        if training:
            augmented_images = self.augmenter(images, training)

            # during training either the original or the augmented images are selected
            # based on self.probability
            augmentation_values = tf.random.uniform(
                shape=(batch_size, 1, 1, 1), minval=0.0, maxval=1.0
            )
            augmentation_bools = tf.math.less(augmentation_values, self.probability)

            images =...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python code you requested.

Adaptive discriminator augmentation
The authors of StyleGAN2-ADA propose to change the
augmentation probability adaptively during training. Though it is explained differently
in the paper, they use integral control on the augmentation
probability to keep the discriminator's accuracy on real images close to a target value.
Note, that their controlled variable is actually the average sign of the discriminator
logits (r_t in the paper), which corresponds to 2 * accuracy - 1.
This method requires two hyperparameters:

target_accuracy: the target value for the discriminator's accuracy on real images. I
recommend selecting its value from the 80-90% range.
integration_steps:
the number of update steps required for an accuracy error of 100% to transform into an
augmentation probability increase of 100%. To give an intuition, this defines how slowly
the augmentation probability is changed. I recommend setting this to a relatively high
value (1000 in this case) so that the augmentation strength is only adjusted slowly.

The main motivation for this procedure is that the optimal value of the target accuracy
is similar across different dataset sizes (see figure 4 and 5 in the paper),
so it does not have to be retuned, because the
process automatically applies stronger data augmentation when it is needed.