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Solve the following python programming problem given the following code:
# Distorts the color distibutions of images
class RandomColorAffine(layers.Layer):
    def __init__(self, brightness=0, jitter=0, **kwargs):
        super().__init__(**kwargs)

        self.brightness = brightness
        self.jitter = jitter

    def get_config(self):
        config = super().get_config()
        config.update({"brightness": self.brightness, "jitter": self.jitter})
        return config

    def call(self, images, training=True):
        if training:
            batch_size = tf.shape(images)[0]

            # Same for all colors
            brightness_scales = 1 + tf.random.uniform(
                (batch_size, 1, 1, 1), minval=-self.brightness, maxval=self.brightness
            )
            # Different for all colors
            jitter_matrices = tf.random.uniform(
                (batch_size, 1, 3, 3), minval=-self.jitter, maxval=self.jitter
            )

            color_transforms = (
                tf.eye(3, batch_shape=[batch_size, 1]) * brightness_scales
                + jitter_matrices
            )
            images = tf.clip_by_value(tf.matmul(images, color_transforms), 0, 1)
        return images

# Image augmentation module
def get_augmenter(min_area, brightness, jitter):
    zoom_factor = 1.0 - math.sqrt(min_area)
    return keras.Sequential(
        [
            keras.Input(shape=(image_size, image_size, image_channels)),
            layers.Rescaling(1 / 255),
            layers.RandomFlip("horizontal"),
            layers.RandomTranslation(zoom_factor / 2, zoom_factor / 2),
            layers.RandomZoom((-zoom_factor, 0.0), (-zoom_factor, 0.0)),
            RandomColorAffine(brightness, jitter),
        ]
    )

def visualize_augmentations(num_images):
    # Sample a batch from a dataset
    images = next(iter(train_dataset))[0][0][:num_images]
    # Apply augmentations
    augmented_images = zip(
        images,
        get_augmenter(**classification_augmentation)(images),
        get_augmenter(**contrastive_augmentation)(images),
       ...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Image augmentations
The two most important image augmentations for contrastive learning are the
following:

Cropping: forces the model to encode different parts of the same image
similarly, we implement it with the
RandomTranslation
and
RandomZoom
layers
Color jitter: prevents a trivial color histogram-based solution to the task by
distorting color histograms. A principled way to implement that is by affine
transformations in color space.

In this example we use random horizontal flips as well. Stronger augmentations
are applied for contrastive learning, along with weaker ones for supervised
classification to avoid overfitting on the few labeled examples.
We implement random color jitter as a custom preprocessing layer. Using
preprocessing layers for data augmentation has the following two advantages:

The data augmentation will run on GPU in batches, so the training will not be
bottlenecked by the data pipeline in environments with constrained CPU
resources (such as a Colab Notebook, or a personal machine)
Deployment is easier as the data preprocessing pipeline is encapsulated in the
model, and does not have to be reimplemented when deploying it