Provides a `structtuple` factory that creates immutable, structurally-typed tuples with caching, slicing, concatenation, and JAX pytree support. This is a self-contained, reusable utility for defining lightweight data containers with robust shape handling, independent of the rest of TFP.

A utility module that tries to compute shapes statically at graph-construction time and falls back to dynamic TensorFlow ops when necessary. It encapsulates the common pattern of preferring static values for performance while maintaining correctness in dynamic contexts. Easily extractable for any TensorFlow or JAX project.

Defines a generic `Reducer` interface with `initialize`, `one_step`, and `finalize` methods for streaming computation of MCMC diagnostics (e.g., R-hat, expectations). This pattern enables efficient, memory-bounded post-processing of Markov chains and is reusable for any streaming aggregation task beyond MCMC.

The abstract base class for all positive semidefinite kernels, providing a consistent API (`apply`, `matrix`, `tensor`) and parameter property descriptors. It demonstrates clean inheritance, matrix construction, and input validation patterns that can be adapted for other kernel-based learning libraries.

Implements `JointDistributionCoroutineAutoBatched`, which automatically infers batch shapes from a probabilistic program written as a generator. This is a non-trivial pattern for composing complex hierarchical models while handling batch dimensions correctly. The design is self-contained and demonstrates advanced use of TensorFlow's `tf.vectorized_map` and shape inference.