Streaming variational inference: out-of-core DataLoader for minibatch ADVI

[YichengYang-Ethan]

Adds a streaming data layer for minibatch variational inference on data that doesn't fit in memory. Ported from pymc-devs/pymc#8325 — moving it here per the discussion on that PR (@ricardoV94's call to start in extras).

pm.Minibatch indexes an in-memory array, so peak memory is O(N). This streams minibatches from an out-of-core source into a pm.Data placeholder instead, so peak memory is set by the batch, the source chunk, and the optional shuffle buffer — independent of N.

The API mirrors torch.utils.data:

• IterableDataset — a re-iterable, out-of-core source of rows (e.g. parquet_source over a directory of shards).
• DataLoader — fixed-size, optionally shuffled minibatches; sized, with len(loader) == N for total_size.
• shuffle_buffer — a bounded shuffle over the stream.

The unbiased-gradient rescaling reuses the existing create_minibatch_rv (the same N / batch_size as pm.Minibatch), via total_size=len(loader).

Notes:

• Lives at pymc_extras/variational/streaming.py — happy to move it.
• pyarrow is an optional dependency, imported lazily only for the Parquet source.
• Tests in tests/variational/. End-to-end example: Example: out-of-core minibatch variational inference with DataLoader and Trainer pymc-examples#888.
• The Trainer (Streaming variational inference: Trainer for minibatch ADVI pymc#8333) will move over as a follow-up.