class_docstr: A data loader which merges data objects from a
  :class:`torch_geometric.data.Dataset` to a mini-batch.
  Data objects can be either of type :class:`~torch_geometric.data.Data` or
  :class:`~torch_geometric.data.HeteroData`.
  
  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`)
      follow_batch (List[str], optional): Creates assignment batch
          vectors for each key in the list. (default: :obj:`None`)
      exclude_keys (List[str], optional): Will exclude each key in the
          list. (default: :obj:`None`)
      **kwargs (optional): Additional arguments of
          :class:`torch.utils.data.DataLoader`.
code: def __init__(
      self,
      dataset: Union[Dataset, Sequence[BaseData], DatasetAdapter],
      batch_size: int = 1,
      shuffle: bool = False,
      follow_batch: Optional[List[str]] = None,
      exclude_keys: Optional[List[str]] = None,
      **kwargs,
  ):
      # Remove for PyTorch Lightning:
      kwargs.pop("collate_fn", None)
  
      # Save for PyTorch Lightning < 1.6:
      self.follow_batch = follow_batch
      self.exclude_keys = exclude_keys
  
      super().__init__(
          dataset,
          batch_size,
          shuffle,
          collate_fn=Collater(dataset, follow_batch, exclude_keys),
          **kwargs,
      )
  
file_path: pytorch_geometric/torch_geometric/loader/dataloader.py
imports: from collections.abc import Mapping
  from typing import Any, List, Optional, Sequence, Union
  import torch.utils.data
  from torch.utils.data.dataloader import default_collate
  from torch_geometric.data import Batch, Dataset
  from torch_geometric.data.data import BaseData
  from torch_geometric.data.datapipes import DatasetAdapter
  from torch_geometric.typing import TensorFrame, torch_frame
label_desc: Use image node_id 1 to create a new DataLoader object from inherited base classes: torch.utils.data with example: obj = DataLoader(dataset, batch_size, shuffle, follow_batch, exclude_keys)