code: import torch
  import torch.nn.functional as F
  from torch.nn import Linear
  from torch_geometric.nn import SAGEConv, global_mean_pool
  
  
  class NestedGraphSAGE(torch.nn.Module):
      def __init__(self, dataset, num_layers, hidden, use_z=False, use_rd=False):
          super(NestedGraphSAGE, self).__init__()
          self.use_rd = use_rd
          self.use_z = use_z
          if self.use_rd:
              self.rd_projection = torch.nn.Linear(1, 8)
          if self.use_z:
              self.z_embedding = torch.nn.Embedding(1000, 8)
          input_dim = dataset.num_features
          if self.use_z or self.use_rd:
              input_dim += 8
  
          self.conv1 = SAGEConv(input_dim, hidden)
          self.convs = torch.nn.ModuleList()
          for i in range(num_layers - 1):
              self.convs.append(SAGEConv(hidden, hidden))
          self.lin1 = torch.nn.Linear(num_layers * hidden, hidden)
          self.lin2 = Linear(hidden, dataset.num_classes)
  
      def reset_parameters(self):
          if self.use_rd:
              self.rd_projection.reset_parameters()
          if self.use_z:
              self.z_embedding.reset_parameters()
          self.conv1.reset_parameters()
          for conv in self.convs:
              conv.reset_parameters()
          self.lin1.reset_parameters()
          self.lin2.reset_parameters()
  
      def forward(self, data):
          x, edge_index, batch = data.x, data.edge_index, data.batch
  
          # node label embedding
          z_emb = 0
          if self.use_z and 'z' in data:
              ### computing input node embedding
              z_emb = self.z_embedding(data.z)
              if z_emb.ndim == 3:
                  z_emb = z_emb.sum(dim=1)
          
          if self.use_rd and 'rd' in data:
              rd_proj = self.rd_projection(data.rd)
              z_emb += rd_proj
  
          if self.use_rd or self.use_z:
              x = torch.cat([z_emb, x], -1)
  
          x = F.relu(self.conv1(x, edge_index))
          xs = [x]
          for conv in self.convs:
              x = F.relu(conv(x, edge_index))
              xs += [x]
          x = global_mean_pool(torch.cat(xs, dim=1), data.node_to_subgraph)
          x = global_mean_pool(x, data.subgraph_to_graph)
          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)
  
      def __repr__(self):
          return self.__class__.__name__
  
  
  class GraphSAGE(torch.nn.Module):
      def __init__(self, dataset, num_layers, hidden, *args, **kwargs):
          super(GraphSAGE, self).__init__()
          self.conv1 = SAGEConv(dataset.num_features, hidden)
          self.convs = torch.nn.ModuleList()
          for i in range(num_layers - 1):
              self.convs.append(SAGEConv(hidden, hidden))
          self.lin1 = torch.nn.Linear(num_layers * hidden, hidden)
          self.lin2 = Linear(hidden, dataset.num_classes)
  
      def reset_parameters(self):
          self.conv1.reset_parameters()
          for conv in self.convs:
              conv.reset_parameters()
          self.lin1.reset_parameters()
          self.lin2.reset_parameters()
  
      def forward(self, data):
          x, edge_index, batch = data.x, data.edge_index, data.batch
          x = F.relu(self.conv1(x, edge_index))
          xs = [x]
          for conv in self.convs:
              x = F.relu(conv(x, edge_index))
              xs += [x]
          x = global_mean_pool(torch.cat(xs, dim=1), batch)
          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)
  
      def __repr__(self):
          return self.__class__.__name__
  
  
  class GraphSAGEWithoutJK(torch.nn.Module):
      def __init__(self, dataset, num_layers, hidden):
          super(GraphSAGEWithoutJK, self).__init__()
          self.conv1 = SAGEConv(dataset.num_features, hidden)
          self.convs = torch.nn.ModuleList()
          for i in range(num_layers - 1):
              self.convs.append(SAGEConv(hidden, hidden))
          self.lin1 = Linear(hidden, hidden)
          self.lin2 = Linear(hidden, dataset.num_classes)
  
      def reset_parameters(self):
          self.conv1.reset_parameters()
          for conv in self.convs:
              conv.reset_parameters()
          self.lin1.reset_parameters()
          self.lin2.reset_parameters()
  
      def forward(self, data):
          x, edge_index, batch = data.x, data.edge_index, data.batch
          x = F.relu(self.conv1(x, edge_index))
          for conv in self.convs:
              x = F.relu(conv(x, edge_index))
          x = global_mean_pool(x, batch)
          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)
  
      def __repr__(self):
          return self.__class__.__name__
  
apis: torch.nn.functional.log_softmax torch.nn.Linear torch.nn.functional.dropout torch.nn.Embedding torch.nn.ModuleList torch.cat