text: import torch
  import torch.nn.functional as F
  from torch.nn import Linear
  from torch_geometric.nn import SAGEConv, global_mean_pool, JumpingKnowledge
  
  
  class GraphSAGE(torch.nn.Module):
      def __init__(self, dataset, num_layers, hidden):
          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 = 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__
  
  
  class GraphSAGEWithJK(torch.nn.Module):
      def __init__(self, dataset, num_layers, hidden):
          super(GraphSAGEWithJK, 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.jump = JumpingKnowledge(mode='cat')
          self.lin1 = 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.jump.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 = self.jump(xs)
          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__
  