class_docstr: Sampled graph data loader. Wrap a :class:`~cugraph_dgl.CuGraphStorage` and a
  :class:`~cugraph_dgl.dataloading.NeighborSampler` into
  an iterable over mini-batches of samples. cugraph_dgl's ``DataLoader`` extends
  PyTorch's ``DataLoader`` by handling creation and
  transmission of graph samples.
class_docstr_tok: ["Sampled","graph","data","loader",".","Wrap","a",":","class",":","`","~cugraph_dgl.CuGraphStorage","`","and","a",":","class",":","`","~cugraph_dgl.dataloading.NeighborSampler","`","into","an","iterable","over","mini-batches","of","samples",".","cugraph_dgl","'s","``","DataLoader","``","extends","PyTorch","'s","``","DataLoader","``","by","handling","creation","and","transmission","of","graph","samples","."]
code: def __del__(self):
      if self.use_ddp:
          torch.distributed.barrier()
      if self._rank == 0:
          if self.use_ddp:
              client = default_client()
              client.unpublish_dataset("cugraph_dgl_mg_graph_ds")
              self._graph_creation_event.clear()
          _clean_directory(self._sampling_output_dir)
  
file_path: cugraph/python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py
imports: from __future__ import annotations
  import os
  import shutil
  import cugraph_dgl
  import cupy
  import cudf
  from cugraph.utilities.utils import import_optional
  from cugraph.gnn import BulkSampler
  from dask.distributed import default_client, Event
  from cugraph_dgl.dataloading import HomogenousBulkSamplerDataset, HeterogenousBulkSamplerDataset
  from cugraph_dgl.dataloading.utils.extract_graph_helpers import create_cugraph_graph_from_edges_dict
label_desc: Use image node_id 3 for calling the DataLoader obj's underlying member method code with example usage: obj.__del__() without return types