class_docstr: The Adaptive Structure Aware Pooling operator from the
  `"ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical
  Graph Representations" <https://arxiv.org/abs/1911.07979>`_ paper.
  
  Args:
      in_channels (int): Size of each input sample.
      ratio (float or int): Graph pooling ratio, which is used to compute
          :math:`k = \lceil \mathrm{ratio} \cdot N \rceil`, or the value
          of :math:`k` itself, depending on whether the type of :obj:`ratio`
          is :obj:`float` or :obj:`int`. (default: :obj:`0.5`)
      GNN (torch.nn.Module, optional): A graph neural network layer for
          using intra-cluster properties.
          Especially helpful for graphs with higher degree of neighborhood
          (one of :class:`torch_geometric.nn.conv.GraphConv`,
          :class:`torch_geometric.nn.conv.GCNConv` or
          any GNN which supports the :obj:`edge_weight` parameter).
          (default: :obj:`None`)
      dropout (float, optional): Dropout probability of the normalized
          attention coefficients which exposes each node to a stochastically
          sampled neighborhood during training. (default: :obj:`0`)
      negative_slope (float, optional): LeakyReLU angle of the negative
          slope. (default: :obj:`0.2`)
      add_self_loops (bool, optional): If set to :obj:`True`, will add self
          loops to the new graph connectivity. (default: :obj:`False`)
      **kwargs (optional): Additional parameters for initializing the
          graph neural network layer.
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docstr: Resets all learnable parameters of the module.
docstr_tok: ["Resets","all","learnable","parameters","of","the","module","."]
code: def reset_parameters(self):
      r"""Resets all learnable parameters of the module."""
      self.lin.reset_parameters()
      self.att.reset_parameters()
      self.gnn_score.reset_parameters()
      if self.gnn_intra_cluster is not None:
          self.gnn_intra_cluster.reset_parameters()
      self.select.reset_parameters()
  
file_path: pytorch_geometric/torch_geometric/nn/pool/asap.py
imports: from typing import Callable, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import Linear
  from torch_geometric.nn import LEConv
  from torch_geometric.nn.pool.select import SelectTopK
  from torch_geometric.utils import add_remaining_self_loops, remove_self_loops, scatter, softmax, to_edge_index, to_torch_coo_tensor, to_torch_csr_tensor
label_desc: Use image node_id 2 for calling the ASAPooling obj's underlying member method code with example usage: obj.reset_parameters() without return types