RandAugment layer

keras.layers.RandAugment(
    value_range=(0, 255),
    num_ops=2,
    factor=0.5,
    interpolation="bilinear",
    seed=None,
    data_format=None,
    **kwargs
)

This layer can be thought of as an all-in-one image augmentation layer. The
policy implemented by this layer has been benchmarked extensively and is
effective on a wide variety of datasets.

**Note:** This layer is safe to use inside a [`tf.data`](https://www.tensorflow.org/api_docs/python/tf/data) or `grain` pipeline
(independently of which backend you're using).

References:
    - [RandAugment](https://arxiv.org/abs/1909.13719)

# Arguments
    value_range: The range of values the input image can take.
        Default is `(0, 255)`. Typically, this would be `(0, 1)`
        for normalized images or `(0, 255)` for raw images.
    num_ops: The number of augmentation operations to apply sequentially
        to each image. Default is 2.
    factor: The strength of the augmentation as a normalized value
        between 0 and 1. Default is 0.5.
    interpolation: The interpolation method to use for resizing operations.
        Options include `nearest`, `bilinear`. Default is `bilinear`.
    seed: Integer. Used to create a random seed.

# Example

layer = keras.layers.RandAugment(bounding_box_format="xyxy")
images = np.random.randint(0, 255, (4, 224, 224, 3), dtype="uint8")

bounding_boxes = {
    "boxes": np.array([
        [[10, 20, 100, 150], [50, 60, 200, 250]],
        [[15, 25, 110, 160], [55, 65, 210, 260]],
        [[20, 30, 120, 170], [60, 70, 220, 270]],
        [[25, 35, 130, 180], [65, 75, 230, 280]],
    ], dtype="float32"),
    "labels": np.array([[0, 1], [1, 2], [2, 3], [0, 3]], dtype="int32")
}

labels = keras.ops.one_hot(
    np.array([0, 1, 2, 3]),
    num_classes=4
)

segmentation_masks = np.random.randint(0, 3, (4, 224, 224, 1), dtype="uint8")

output = layer(
    {
        "images": images,
        "bounding_boxes": bounding_boxes,
        "labels": labels,
        "segmentation_masks": segmentation_masks
    },
    training=True
)