Provides a collection of PIL-based image augmentation operators (autocontrast, equalize, posterize, rotate, solarize, shear, translate, color, contrast, brightness, sharpness) with severity-driven parameter scaling. Each operator is a standalone function that takes a PIL image and a severity level, making them reusable across any image augmentation pipeline. The parameter scaling pattern (int_parameter, float_parameter, sample_level) is clean and easily adaptable to new operations.

Implements the core AugMix algorithm in numpy: combines multiple augmentation chains via Dirichlet-distributed weights and a convex mixture with the original image. The function 'augment_and_mix' is well-parameterized (severity, width, depth, alpha) and operates on numpy arrays, making it model-agnostic and easily extractable into other projects (e.g., TensorFlow, JAX) with minimal adaptation.

Contains the 'compute_mce' function that calculates the mean Corruption Error (mCE) normalized by AlexNet baseline errors. This evaluation metric is standard in robustness research and is implemented in a clean, reusable manner. It takes a dictionary of corruption accuracies and returns a single mCE score. Could be extracted as a standalone utility for benchmarking model robustness on ImageNet-C.