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 _collate_fn(
      self, top_layer_target_nodes_indexes_list: _typing.List[int]
  ) -> TargetDependantSampledData:
      return self.__sample_layers(
          torch.tensor(top_layer_target_nodes_indexes_list).unique()
      )
  
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 4 for calling the BasicLayerWiseTargetDependantSampler obj's underlying member method code with example usage: obj._collate_fn(top_layer_target_nodes_indexes_list) and returns: self