In deep learning practice, a single augmentation technique is rarely applied in isolation. Instead, multiple distinct image augmentation methods—such as horizontal flipping, random resized cropping, and color jittering—are combined into a unified transformation pipeline. By applying these diverse transformations sequentially through a composition function (often denoted as Compose), the range of possible random variations per image increases significantly, yielding more robust and invariant models.