class_docstr: A data loader which batches data objects from a
  :class:`torch_geometric.data.dataset` to a :python:`Python` list.
  Data objects can be either of type :class:`~torch_geometric.data.Data` or
  :class:`~torch_geometric.data.HeteroData`.
  
  .. note::
  
      This data loader should be used for multi-GPU support via
      :class:`torch_geometric.nn.DataParallel`.
  
  Args:
      dataset (Dataset): The dataset from which to load the data.
      batch_size (int, optional): How many samples per batch to load.
          (default: :obj:`1`)
      shuffle (bool, optional): If set to :obj:`True`, the data will be
          reshuffled at every epoch. (default: :obj:`False`)
      **kwargs (optional): Additional arguments of
          :class:`torch.utils.data.DataLoader`, such as :obj:`drop_last` or
          :obj:`num_workers`.
code: def __init__(
      self,
      dataset: Union[Dataset, List[BaseData]],
      batch_size: int = 1,
      shuffle: bool = False,
      **kwargs,
  ):
      # Remove for PyTorch Lightning:
      kwargs.pop("collate_fn", None)
  
      super().__init__(
          dataset,
          batch_size=batch_size,
          shuffle=shuffle,
          collate_fn=collate_fn,
          **kwargs,
      )
  
imports: from typing import List, Union
  import torch
  from torch_geometric.data import Dataset
  from torch_geometric.data.data import BaseData
label_desc: Use image node_id 1 to create a new DataListLoader object from inherited base classes: torch.utils.data with example: obj = DataListLoader(dataset, batch_size, shuffle)