Caching support for long-running transformations

To speed up the compilation process for large models or large layers, it would make sense to have a caching mechanism for long-running transformations. The cached outputs would be persistent and get reused when appropriate when a transform is called. The cache generation/reuse should be optional.

The idea would be to generate a hash for all relevant input data for a transform for a particular node (including node attributes, parameter tensors, quantization annotations...) and use that hash as the cache key for the output products in a folder in a persistent location. Later on, if the same transformation is executed on a node with the same hash, the cached outputs can be reused by copying from the persistent cache folder into a new folder.

For NodeLocalTransform the hashing is relatively straightforward as only the node itself is passed to the transformation. For others, it's hard to generalize, and best considered on a case-by-case basis that gives the most execution time benefits for the use cases we have.

@quetric @Tobi-Alonso do you have any suggestions for which transforms to start with to get the most benefit, or any other comments?

Start by tracing the transformation entry points, with NodeLocalTransform as the clearest initial case. Determine which node data must contribute to a cache key, where persistent outputs would live, and how caching would be enabled or disabled. Done means a defined, optional cache design that safely reuses matching outputs; no files or tests are named in the issue.