class_docstr: Description
  -----------
  The graph classification model using HGP-SL pooling.
  
  Parameters
  ----------
  in_feat : int
      The number of input node feature's channels.
  out_feat : int
      The number of output node feature's channels.
  hid_feat : int
      The number of hidden state's channels.
  dropout : float, optional
      The dropout rate. Default: 0
  pool_ratio : float, optional
      The pooling ratio for each pooling layer. Default: 0.5
  conv_layers : int, optional
      The number of graph convolution and pooling layers. Default: 3
  sample : bool, optional
      Whether use k-hop union graph to increase efficiency.
      Currently we only support full graph. Default: :obj:`False`
  sparse : bool, optional
      Use edge sparsemax instead of edge softmax. Default: :obj:`True`
  sl : bool, optional
      Use structure learining module or not. Default: :obj:`True`
  lamb : float, optional
      The lambda parameter as weight of raw adjacency as described in the
      HGP-SL paper. Default: 1.0
code: def forward(self, graph, n_feat):
      final_readout = None
      e_feat = None
  
      for i in range(self.num_layers):
          graph, n_feat, e_feat, readout = self.convpool_layers[i](
              graph, n_feat, e_feat
          )
          if final_readout is None:
              final_readout = readout
          else:
              final_readout = final_readout + readout
  
      n_feat = F.relu(self.lin1(final_readout))
      n_feat = F.dropout(n_feat, p=self.dropout, training=self.training)
      n_feat = F.relu(self.lin2(n_feat))
      n_feat = F.dropout(n_feat, p=self.dropout, training=self.training)
      n_feat = self.lin3(n_feat)
  
      return F.log_softmax(n_feat, dim=-1)
  
file_path: dgl/examples/pytorch/hgp_sl/networks.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn import AvgPooling, MaxPooling
  from layers import ConvPoolReadout
label_desc: Use image node_id 2 for calling the HGPSLModel obj's underlying member method code with example usage: obj.forward(graph, n_feat) and returns: F