Add an end-to-end anomaly detection tutorial using PyTorch Lightning

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### 📚 Documentation

## Motivation

PyTorch Lightning is widely used for supervised learning tasks, but there is currently no end-to-end tutorial demonstrating anomaly detection.

Anomaly detection is an important application in industrial inspection, healthcare, cybersecurity, and time-series monitoring. A beginner-friendly tutorial would help users understand how Lightning can simplify training and evaluation for these workflows.

## Proposed tutorial

I would like to contribute a tutorial that demonstrates:

- Building an Autoencoder (or Variational Autoencoder) using LightningModule
- Organizing datasets with LightningDataModule
- Training only on normal samples
- Computing reconstruction-error anomaly scores
- Selecting an anomaly threshold using validation data
- Evaluating on normal and anomalous samples
- Logging metrics with Lightning loggers
- Visualizing reconstructed images and anomaly scores

The tutorial would follow Lightning's recommended coding style and be suitable for beginners.

## Possible extensions

Depending on maintainer feedback, the tutorial could later include:

- Callbacks for reconstruction visualization
- TensorBoard logging
- Multiple datasets
- Industrial anomaly detection examples

I'd be happy to implement this if the maintainers think it would be a useful addition.

cc @lantiga

No file, test, or entry point is named. Start by reviewing the repository's recommended tutorial coding style and maintainer feedback, then define a beginner-friendly end-to-end example covering the proposed LightningModule, LightningDataModule, training, threshold selection, evaluation, logging, and visualizations; done means the complete tutorial is clear and runnable.

Written by the indexing model from the issue text.