class_docstr: A data loader iterator extended by a simple post transformation
  function :meth:`transform_fn`. While the iterator may request items from
  different sub-processes, :meth:`transform_fn` will always be executed in
  the main process.
  
  This iterator is used in PyG's sampler classes, and is responsible for
  feature fetching and filtering data objects after sampling has taken place
  in a sub-process. This has the following advantages:
  
  * We do not need to share feature matrices across processes which may
    prevent any errors due to too many open file handles.
  * We can execute any expensive post-processing commands on the main thread
    with full parallelization power (which usually executes faster).
  * It lets us naturally support data already being present on the GPU.
code: def __next__(self) -> Any:
      return self.transform_fn(next(self.iterator))
  
file_path: pytorch_geometric/torch_geometric/loader/base.py
imports: from typing import Any, Callable
  from torch.utils.data.dataloader import _BaseDataLoaderIter, _MultiProcessingDataLoaderIter
label_desc: Use image node_id 5 for calling the DataLoaderIterator obj's underlying member method code with example usage: obj.__next__() and returns: self