class_docstr: A uniform aggregation of sampled data for one mini-batch,
  generally sampler by target-dependent sampler.
  Node-wise Sampling and Layer-wise Sampling techniques are definitely target-dependent,
  for which each sampled subgraph depends on the corresponding target nodes.
  Besides, the Subgraph-wise Sampling mechanism can also be treated as target-dependent,
  however, each set of target nodes for Subgraph-wise Sampling is determined by the sampled graph.
  
  Parameters
  ------------
  sampled_edges_for_layers:
      A sequence of tuple denoted as
      `( edge_index_for_sampled_graph, edge_id_in_integral_graph, (optional)edge_weight )`,
      where the `edge_index_for_sampled_graph` represents the sampled `edge_index` for sampled subgraph,
      the `edge_id_in_integral_graph` represents
      the corresponding positional indexes for the `edge_index` of integral graph,
      and the optional `edge_weight` for aggregation can also be provided.
  target_nodes_indexes:
      A tuple consists of (`torch.Tensor`, `torch.Tensor`),
      in which the first element represents the indexes of target nodes in sampled subgraph,
      and the second element represents the indexes of target nodes in the integral graph.
  all_sampled_nodes_indexes:
      Indexes of all sampled nodes for mini-batch.
  
  Attributes
  ------------
  target_nodes_indexes:
      A combined aggregation composed of
      `indexes_in_sampled_graph` and `indexes_in_integral_graph`
  all_sampled_nodes_indexes:
      Indexes of all sampled nodes for mini-batch.
  sampled_edges_for_layers:
      The stored sequence of tuple
      `( edge_index_for_sampled_graph, edge_id_in_integral_graph, (optional)edge_weight )`.
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docstr: indexes of all sampled nodes in the integral graph 
docstr_tok: ["indexes","of","all","sampled","nodes","in","the","integral","graph"]
code: def all_sampled_nodes_indexes(self) -> torch.LongTensor:
      """indexes of all sampled nodes in the integral graph"""
      all_sampled_nodes_indexes: _typing.Any = (
          self.__all_sampled_nodes_indexes
      )
      return all_sampled_nodes_indexes
  
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 TargetDependantSampledData obj's underlying member method code with example usage: obj.all_sampled_nodes_indexes() and returns: all_sampled_nodes_indexes