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 __iter__(self):
      output_dir = os.path.join(
          self._sampling_output_dir, "epoch_" + str(self.epoch_number)
      )
      kwargs = {}
      if isinstance(
          self.cugraph_dgl_dataset, HomogenousBulkSamplerDataset
      ):
          kwargs["deduplicate_sources"] = True
          kwargs["prior_sources_behavior"] = "carryover"
          kwargs["renumber"] = True
  
          if self.sparse_format == "csc":
              kwargs["compression"] = "CSR"
              kwargs["compress_per_hop"] = True
              # The following kwargs will be deprecated in uniform sampler.
              kwargs["use_legacy_names"] = False
              kwargs["include_hop_column"] = False
  
      else:
          kwargs["deduplicate_sources"] = False
          kwargs["prior_sources_behavior"] = None
          kwargs["renumber"] = False
  
      bs = BulkSampler(
          output_path=output_dir,
          batch_size=self._batch_size,
          graph=self._cugraph_graph,
          batches_per_partition=self._batches_per_partition,
          seeds_per_call=self._seeds_per_call,
          fanout_vals=self.graph_sampler._reversed_fanout_vals,
          with_replacement=self.graph_sampler.replace,
          **kwargs,
      )
  
      if self.shuffle:
          self.tensorized_indices_ds.shuffle()
  
      batch_df = create_batch_df(self.tensorized_indices_ds)
      bs.add_batches(
          batch_df, start_col_name="start", batch_col_name="batch_id"
      )
      bs.flush()
      self.cugraph_dgl_dataset.set_input_files(
          input_directory=output_dir
      )
      self.epoch_number = self.epoch_number + 1
      return super().__iter__()
  
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 2 for calling the DataLoader obj's underlying member method code with example usage: obj.__iter__() and returns: super