cleanup

other_packages/python/tiledbsoma_ml/src/tiledbsoma_ml/pytorch.py

@@ -344,12 +344,6 @@ def __len__(self) -> int:
  Lifecycle:
  experimental
  """
- # self._init_once()
- # assert self._obs_joinids is not None
- # world_size, _ = _get_distributed_world_rank()
- # n_workers, _ = _get_worker_world_rank()
- # div, rem = divmod(len(self._obs_joinids) // world_size, self.batch_size)
- # return div + bool(rem)
  return self.shape[0]
 
  @property
@@ -795,7 +789,6 @@ def shape(self) -> Tuple[int, int]:
 
 def experiment_dataloader(
  ds: torchdata.datapipes.iter.IterDataPipe | torch.utils.data.IterableDataset,
- # num_workers: int = 0,
  **dataloader_kwargs: Any,
 ) -> torch.utils.data.DataLoader:
  """Factory method for :class:`torch.utils.data.DataLoader`. This method can be used to safely instantiate a
@@ -813,8 +806,6 @@ def experiment_dataloader(
  ds:
  A :class:`torch.utils.data.IterableDataset` or a :class:`torchdata.datapipes.iter.IterDataPipe`. May
  include chained data pipes.
- num_workers:
- How many subprocesses to use for data loading. 0 means that the data will be loaded in the main process. (default: 0)
  **dataloader_kwargs:
  Additional keyword arguments to pass to the :class:`torch.utils.data.DataLoader` constructor,
  except for ``shuffle``, ``batch_size``, ``sampler``, and ``batch_sampler``, which are not