class_docstr: The base class for various Layer-wise Sampling techniques,
  providing basic functionality of composing sampled data for mini-batches.
  
  Parameters
  ------------
  edge_index:
      edge index of integral graph
  target_nodes_indexes:
      indexes of target nodes in the integral graph
  layer_wise_arguments:
      layer-wise arguments for sampling
  batch_size:
      batch size for target nodes
  num_workers:
      number of workers
  shuffle:
      flag for shuffling, default to True
  kwargs:
      remaining keyword arguments
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code: def __init__(
      self,
      edge_index: torch.LongTensor,
      target_nodes_indexes: torch.LongTensor,
      layer_wise_arguments: _typing.Sequence,
      batch_size: _typing.Optional[int] = 1,
      num_workers: int = 0,
      shuffle: bool = True,
      **kwargs,
  ):
      self._edge_index: torch.LongTensor = edge_index
      self.__layer_wise_arguments: _typing.Sequence = (
          layer_wise_arguments
      )
      if "collate_fn" in kwargs:
          del kwargs["collate_fn"]
      super(BasicLayerWiseTargetDependantSampler, self).__init__(
          target_nodes_indexes.unique().numpy(),
          batch_size,
          shuffle,
          num_workers=num_workers,
          collate_fn=self._collate_fn,
          **kwargs,
      )
  
file_path: AutoGL/autogl/module/train/sampling/sampler/target_dependant_sampler.py
imports: import torch.utils.data
  import typing
label_desc: Use image node_id 1 to create a new BasicLayerWiseTargetDependantSampler object from inherited base classes: TargetDependantSampler with example: obj = BasicLayerWiseTargetDependantSampler(edge_index, target_nodes_indexes, layer_wise_arguments, batch_size, num_workers, shuffle)