请帮我写个代码：一个基于GCN的编码器和一个基于转置GCN的解码器；其中训练集的输入有71个节点和209条边, 输出有71个节点和242条边；测试集的输入有64个节点和189条边，最终输出预测结果。

好的，以下是一个基于PyTorch实现的GCN编码器和TGCN解码器的模型代码，以及训练和测试的代码。请注意，这只是一个简单的示例，你可能需要根据自己的数据和需求进行调整：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, GATConv, global_mean_pool

class GCNEncoder(nn.Module):
    def __init__(self, in_channels, hidden_channels):
        super(GCNEncoder, self).__init__()
        self.conv1 = GCNConv(in_channels, hidden_channels)
        self.conv2 = GCNConv(hidden_channels, hidden_channels)
        
    def forward(self, x, edge_index):
        x = F.relu(self.conv1(x, edge_index))
        x = F.relu(self.conv2(x, edge_index))
        return x

class TGCNDecoder(nn.Module):
    def __init__(self, hidden_channels, out_channels):
        super(TGCNDecoder, self).__init__()
        self.conv1 = GCNConv(hidden_channels, hidden_channels)
        self.conv2 = GCNConv(hidden_channels, out_channels)
        
    def forward(self, x, edge_index):
        x = F.relu(self.conv1(x, edge_index.t()))
        x = self.conv2(x, edge_index.t())
        return x

class GCNModel(nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels):
        super(GCNModel, self).__init__()
        self.encoder = GCNEncoder(in_channels, hidden_channels)
        self.decoder = TGCNDecoder(hidden_channels, out_channels)
        
    def forward(self, x, edge_index):
        x = self.encoder(x, edge_index)
        x = self.decoder(x, edge_index)
        return x

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = GCNModel(in_channels=71, hidden_channels=32, out_channels=71).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
criterion = nn.MSELoss()

def train(model, optimizer, criterion, x, edge_index, y):
    model.train()
    optimizer.zero_grad()
    out = model(x, edge_index)
    loss = criterion(out, y)
    loss.backward()
    optimizer.step()
    return loss.item()

def test(model, x, edge_index):
    model.eval()
    with torch.no_grad():
        out = model(x, edge_index)
    return out.cpu().numpy()

# 训练集输入
train_x = torch.randn(71, 10).to(device)
train_edge_index = torch.tensor([[0, 1, 2, ...], [1, 2, 3, ...]], dtype=torch.long).to(device)
# 训练集输出
train_y = torch.randn(71, 10).to(device)
# 测试集输入
test_x = torch.randn(64, 10).to(device)
test_edge_index = torch.tensor([[0, 1, 2, ...], [1, 2, 3, ...]], dtype=torch.long).to(device)

# 训练
for epoch in range(100):
    loss = train(model, optimizer, criterion, train_x, train_edge_index, train_y)
    print('Epoch {:03d}, Loss: {:.4f}'.format(epoch, loss))
    
# 测试
test_out = test(model, test_x, test_edge_index)
print(test_out)

请注意，上述代码中使用了PyTorch Geometric库中的GCNConv和global_mean_pool函数来实现GCN编码器。如果你的数据格式不同，你可能需要修改这部分代码。另外，如果你的数据中有节点特征，你也需要将其作为GCNConv的输入。