ml_switcheroo.frameworks.common.data
====================================

.. py:module:: ml_switcheroo.frameworks.common.data

.. autoapi-nested-parse::

   Data Loader Standard & Runtime Shim.

   This module defines the **Generic Data Loader Shim** used when transpiling
   PyTorch `DataLoader` code to frameworks that lack a direct equivalent (like JAX or NumPy).
   It also provides the Semantic Configuration injection to ensure the engine detects
   the DataLoader API.

   Capabilities handled by the Shim:
   1.  **Batching**: `batch_size`.
   2.  **Shuffling**: `shuffle`.
   3.  **Dropping Last**: `drop_last`.
   4.  **Dataset Protocol**: Supports `__len__` and `__getitem__`.
   5.  **Multi-Processing Stubs**: `num_workers`, `pin_memory`, `persistent_workers`
       are accepted as no-ops to ensure compatibility with performance-tuned Torch code.

   The Shim is designed to be a lightweight iterator yielding collated batches.



Functions
---------

.. autoapisummary::

   ml_switcheroo.frameworks.common.data.get_dataloader_semantics
   ml_switcheroo.frameworks.common.data.get_shim_code


Module Contents
---------------

.. py:function:: get_dataloader_semantics() -> Dict[str, Any]

   Returns the Semantic Definition for the DataLoader.

   Now includes performance arguments found in standard Torch examples.
   These are mapped to the Shim, which handles them gracefully (usually ignoring them).


.. py:function:: get_shim_code() -> str

   Returns the source code for the `GenericDataLoader` class.
   This code is injected into generated files by the `convert_dataloader` plugin.

   Updates:
   - Added `num_workers`, `pin_memory`, `persistent_workers` to __init__.
   - Included `collate_fn` stub support.