Initial PySpark support

- Implement read/write storage functions to handle both directory and single file formats
- Selection logic for deciding when to read inputs as Pandas vs PySpark
- Hooks for repartitioning / flattening between storage and presenting data to models
- Implement main context methods for get/put PySpark
- Implicit conversion - data items loaded as Pandas automatically converted if requested as PySpark, reverse is available with a row limit
- Run example PySpark model from doc folder as end-to-end validation

Start with the PySpark model example in the doc folder, then trace the existing storage functions and main context get/put methods it exercises. Validate directory and single-file reads and writes, Pandas/PySpark selection and conversions, repartitioning and flattening, and the documented example end to end.

Written by the indexing model from the issue text.