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
class_docstr_tok: ["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"]
docstr: Sample edges for one specific layer, expected to be implemented in subclass.
  
  Parameters
  ------------
  __current_layer_target_nodes_indexes:
      target nodes for current layer
  __top_layer_target_nodes_indexes:
      target nodes for top layer
  layer_argument:
      argument for current layer
  args:
      remaining positional arguments
  kwargs:
      remaining keyword arguments
  
  Returns
  --------
  edge_id_in_integral_graph:
      the corresponding positional indexes for the `edge_index` of integral graph
  edge_weight:
      the optional `edge_weight` for aggregation
docstr_tok: ["Sample","edges","for","one","specific","layer",",","expected","to","be","implemented","in","subclass",".","Parameters","--","--","--","--","--","--","__current_layer_target_nodes_indexes",":","target","nodes","for","current","layer","__top_layer_target_nodes_indexes",":","target","nodes","for","top","layer","layer_argument",":","argument","for","current","layer","args",":","remaining","positional","arguments","kwargs",":","remaining","keyword","arguments","Returns","--","--","--","--","edge_id_in_integral_graph",":","the","corresponding","positional","indexes","for","the","`","edge_index","`","of","integral","graph","edge_weight",":","the","optional","`","edge_weight","`","for","aggregation"]
code: def _sample_edges_for_layer(
      self,
      __current_layer_target_nodes_indexes: torch.LongTensor,
      __top_layer_target_nodes_indexes: torch.LongTensor,
      layer_argument: _typing.Any,
      *args,
      **kwargs,
  ) -> _typing.Tuple[torch.LongTensor, _typing.Optional[torch.Tensor]]:
      """
      Sample edges for one specific layer, expected to be implemented in subclass.
  
      Parameters
      ------------
      __current_layer_target_nodes_indexes:
          target nodes for current layer
      __top_layer_target_nodes_indexes:
          target nodes for top layer
      layer_argument:
          argument for current layer
      args:
          remaining positional arguments
      kwargs:
          remaining keyword arguments
  
      Returns
      --------
      edge_id_in_integral_graph:
          the corresponding positional indexes for the `edge_index` of integral graph
      edge_weight:
          the optional `edge_weight` for aggregation
      """
      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 3 for calling the BasicLayerWiseTargetDependantSampler obj's underlying member method code with example usage: obj._sample_edges_for_layer(__current_layer_target_nodes_indexes, __top_layer_target_nodes_indexes, layer_argument) without return types