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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docstr: :param edge_index: edge index of integral graph
  :param target_nodes_indexes: indexes of target nodes in the integral graph
  :param layer_wise_arguments: layer-wise arguments for sampling
  :param batch_size: batch size for target nodes
  :param num_workers: number of workers
  :param shuffle: flag for shuffling, default to True
  :param args: remaining positional arguments
  :param kwargs: remaining keyword arguments
  :return: instance of TargetDependantSampler
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code: def create_basic_sampler(
      cls,
      edge_index: torch.LongTensor,
      target_nodes_indexes: torch.LongTensor,
      layer_wise_arguments: _typing.Sequence,
      batch_size: int = 1,
      num_workers: int = 0,
      shuffle: bool = True,
      *args,
      **kwargs,
  ) -> TargetDependantSampler:
      """
      :param edge_index: edge index of integral graph
      :param target_nodes_indexes: indexes of target nodes in the integral graph
      :param layer_wise_arguments: layer-wise arguments for sampling
      :param batch_size: batch size for target nodes
      :param num_workers: number of workers
      :param shuffle: flag for shuffling, default to True
      :param args: remaining positional arguments
      :param kwargs: remaining keyword arguments
      :return: instance of TargetDependantSampler
      """
      return BasicLayerWiseTargetDependantSampler(
          edge_index,
          target_nodes_indexes,
          layer_wise_arguments,
          batch_size,
          num_workers,
          shuffle,
          **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 2 for calling the BasicLayerWiseTargetDependantSampler obj's underlying member method code with example usage: obj.create_basic_sampler(cls, edge_index, target_nodes_indexes, layer_wise_arguments, batch_size, num_workers, shuffle) and returns: BasicLayerWiseTargetDependantSampler