code: import torch
  import torch.nn.functional as F
  from torch.nn import Linear
  from torch_geometric.nn import (ASAPooling,
                                  GraphConv, global_mean_pool,
                                  JumpingKnowledge)
  
  
  class ASAP(torch.nn.Module):
      def __init__(self, num_vocab, max_seq_len, node_encoder, emb_dim, num_layers, hidden, ratio=0.8, dropout=0, num_class=0):
          super(ASAP, self).__init__()
  
          self.num_class = num_class
          self.max_seq_len = max_seq_len
          self.node_encoder = node_encoder
  
          self.conv1 = GraphConv(emb_dim, hidden, aggr='mean')
          self.convs = torch.nn.ModuleList()
          self.pools = torch.nn.ModuleList()
          self.convs.extend([
              GraphConv(hidden, hidden, aggr='mean')
              for i in range(num_layers - 1)
          ])
          self.pools.extend([
              ASAPooling(hidden, ratio, dropout=dropout)
              for i in range((num_layers) // 2)
          ])
          self.jump = JumpingKnowledge(mode='cat')
          self.lin1 = Linear(num_layers * hidden, hidden)
          # self.lin2 = Linear(hidden, dataset.num_classes)
  
          if self.num_class > 0:  # classification
              self.graph_pred_linear = torch.nn.Linear(hidden, self.num_class)
          else:
              self.graph_pred_linear_list = torch.nn.ModuleList()
              for i in range(max_seq_len):
                  self.graph_pred_linear_list.append(torch.nn.Linear(hidden, num_vocab))
  
      def reset_parameters(self):
          self.conv1.reset_parameters()
          for conv in self.convs:
              conv.reset_parameters()
          for pool in self.pools:
              pool.reset_parameters()
          self.lin1.reset_parameters()
          self.lin2.reset_parameters()
  
      def forward(self, data):
          x, edge_index, node_depth, batch = data.x, data.edge_index, data.node_depth, data.batch
  
          x = self.node_encoder(x, node_depth.view(-1, ))
  
          edge_weight = None
          x = F.relu(self.conv1(x, edge_index))
          xs = [global_mean_pool(x, batch)]
          for i, conv in enumerate(self.convs):
              x = conv(x=x, edge_index=edge_index, edge_weight=edge_weight)
              x = F.relu(x)
              xs += [global_mean_pool(x, batch)]
              if i % 2 == 0 and i < len(self.convs) - 1:
                  pool = self.pools[i // 2]
                  x, edge_index, edge_weight, batch, _ = pool(
                      x=x, edge_index=edge_index, edge_weight=edge_weight,
                      batch=batch)
          x = self.jump(xs)
          x = F.relu(self.lin1(x))
          x = F.dropout(x, p=0.5, training=self.training)
          # x = self.lin2(x)
          # return F.log_softmax(x, dim=-1)
  
          if self.num_class > 0:
              return self.graph_pred_linear(x)
  
          pred_list = []
          for i in range(self.max_seq_len):
              pred_list.append(self.graph_pred_linear_list[i](x))
          return pred_list
  
      def __repr__(self):
          return self.__class__.__name__
  
  
apis: torch.nn.Linear torch.nn.ModuleList torch.nn.functional.dropout torch.nn.functional.relu