class_docstr: An abstract base class for various target-dependent sampler
class_docstr_tok: ["An","abstract","base","class","for","various","target-dependent","sampler"]
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, default to 1
  :param num_workers: number of workers, default to 0
  :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, default to 1
      :param num_workers: number of workers, default to 0
      :param shuffle: flag for shuffling, default to True
      :param args: remaining positional arguments
      :param kwargs: remaining keyword arguments
      :return: instance of TargetDependantSampler
      """
      raise NotImplementedError
  
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 for calling the TargetDependantSampler 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) without return types