class_docstr: Obsolete implementation, unused
class_docstr_tok: ["Obsolete","implementation",",","unused"]
code: def __init__(
      self,
      edge_index: torch.LongTensor,
      target_nodes_indexes: torch.LongTensor,
      layer_wise_arguments: _typing.Sequence,
      batch_size: _typing.Optional[int] = 1,
      num_workers: int = 0,
      shuffle: bool = True,
      **kwargs,
  ):
      super().__init__(
          torch_geometric.utils.add_remaining_self_loops(edge_index)[0],
          target_nodes_indexes,
          layer_wise_arguments,
          batch_size,
          num_workers,
          shuffle,
          **kwargs,
      )
      self.__all_edge_weights: torch.Tensor = (
          self._Utility.compute_edge_weights(self._edge_index)
      )
  
file_path: AutoGL/autogl/module/train/sampling/sampler/layer_dependent_importance_sampler.py
imports: import numpy
  import scipy.sparse
  import torch
  import torch.utils.data
  import typing
  import torch_geometric
  from .None import target_dependant_sampler
label_desc: Use image node_id 1 to create a new _LayerDependentImportanceSampler object from inherited base classes: target_dependant_sampler with example: obj = _LayerDependentImportanceSampler(edge_index, target_nodes_indexes, layer_wise_arguments, batch_size, num_workers, shuffle)