Implements a reusable EarlyStopping class with configurable patience, min_delta, and restore_best_weights. Encapsulates a common training pattern that can be extracted and applied to any PyTorch model. Demonstrates clean encapsulation and metric-based stopping logic.

Contains a self-contained RevIN (Reversible Instance Normalization) module and PositionalEncoding class for time-series transformers. These are generic, well-typed PyTorch modules that can be reused in any univariate/multivariate forecasting project. The transformer architecture (encoder-only with causal masking) is a clean template.

A model-agnostic perturbation_rank function that computes feature importance by shuffling each column and measuring loss increase. Works for regression and classification, is independent of model architecture, and uses efficient vectorized operations. Easily extractable as a standalone utility.

Implements a blend_ensemble function that combines predictions from multiple PyTorch and scikit-learn models via logistic regression stacking. The function is generic (accepts any list of models and training data) and demonstrates a professional ensemble pattern without hardcoding model internals.

Provides a complete Autoencoder class and training loop for anomaly detection on the digits dataset. The pattern (autoencoder → reconstruction error → anomaly threshold) is reusable for any unsupervised anomaly detection task. The code separates encoder, decoder, and detection logic cleanly.