Provides LabelEncoder and LabelManager classes for handling categorical labels in augmentation pipelines. LabelEncoder supports incremental fitting, unknown label handling, and background class specification. LabelManager wraps multiple encoders for different label fields. This is a self-contained, reusable component for any ML pipeline that needs to encode/decode categorical labels during data augmentation, with minimal dependencies (numpy, typing).

Implements multiple dithering algorithms: ordered dithering (Bayer matrices up to 8x8), error diffusion (Floyd-Steinberg, Jarvis, Stucki, Atkinson, Burkes, Sierra variants), and random dithering. All functions are self-contained (only numpy and typing), well-documented, and optimized for uint8 images. This module can be extracted as a standalone dithering library for image processing tasks.

Implements a TelemetryClient singleton with rate limiting, deduplication, environment detection (CI, Colab, Jupyter, pytest), and opt-out via environment variable or settings file. It collects pipeline structure, environment info, and session data asynchronously. The pattern of a configurable, privacy-aware telemetry client with backends (e.g., Mixpanel) is reusable across projects.

A standalone CI planning tool that classifies changed file paths into risk domains (e.g., core, transforms, tests, docs) and selects appropriate checks (lint, type-check, contracts, benchmarks, etc.). It produces a CIPlan dataclass with gates, benchmarks, and shard configuration. This pattern is reusable for any project that needs dynamic CI orchestration based on file changes.