我有一个形状是（307，16992）的交通数据集，我现在已经把它变成了形状为（7，1358，12，307，2）的交通数据集，其中7是槽的数量，307是交通传感器节点个数。0维是速度特征，1维是根据邻接矩阵划分的度。我现在想利用GAT作为VAE的编码器，对度一致的节点计算注意力系数，这个pytorch代码怎么写？另外，我需要构造一个3层MLP来作为另一编码器，对每个槽中的数据计算隐变量，这个代码又该怎么写？注意力网络那边用点注意力机制，我的数据集太大了，经常出现被杀的情况，但是我不知道怎么修改？我该怎么成功运行并且避免内存爆炸的情况？请给我一个能成功运行的实例，且避免kill和内存爆炸的实例代码 pytorch。另外GAT部分修改一下。上述代码并没有体现出计算每个度相同的节点之间的注意力系数，我想通过一个for循环计算度相同节点之间的注意力系数，这该怎么做呢。同时我需要它的输出和MLP的输出维度保持一致，这样两个编码器的隐变量就可以拼接或相加起来

以下是一个可能的代码实现，注意其中一些超参数需要根据你的具体情况进行调整：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GATConv

class Encoder(nn.Module):
    def __init__(self, in_channels, out_channels, hidden_channels, num_layers):
        super(Encoder, self).__init__()
        self.num_layers = num_layers
        self.gat_layers = nn.ModuleList()
        self.mlp_layers = nn.ModuleList()
        
        # GAT layers
        for i in range(num_layers):
            self.gat_layers.append(
                GATConv(in_channels=in_channels, out_channels=hidden_channels, heads=8)
            )
            in_channels = hidden_channels * 8
        
        # MLP layers
        for i in range(num_layers):
            self.mlp_layers.append(
                nn.Linear(in_features=in_channels, out_features=hidden_channels)
            )
            in_channels = hidden_channels
        
        # Variational Autoencoder
        self.mu = nn.Linear(in_channels, out_channels)
        self.log_var = nn.Linear(in_channels, out_channels)
    
    def forward(self, x, edge_index):
        # GAT layers
        for i in range(self.num_layers):
            x = self.gat_layers[i](x, edge_index)
            x = F.elu(x)
        
        # MLP layers
        for i in range(self.num_layers):
            x = self.mlp_layers[i](x)
            x = F.elu(x)
        
        # Variational Autoencoder
        mu = self.mu(x)
        log_var = self.log_var(x)
        std = torch.exp(0.5 * log_var)
        eps = torch.randn_like(std)
        z = mu + eps * std
        
        return z, mu, log_var

class MLP(nn.Module):
    def __init__(self, in_channels, out_channels, hidden_channels, num_layers):
        super(MLP, self).__init__()
        self.num_layers = num_layers
        self.layers = nn.ModuleList()
        
        for i in range(num_layers):
            self.layers.append(
                nn.Linear(in_features=in_channels, out_features=hidden_channels)
            )
            in_channels = hidden_channels
        
        self.mu = nn.Linear(in_channels, out_channels)
        self.log_var = nn.Linear(in_channels, out_channels)
    
    def forward(self, x):
        for i in range(self.num_layers):
            x = self.layers[i](x)
            x = F.elu(x)
        
        mu = self.mu(x)
        log_var = self.log_var(x)
        std = torch.exp(0.5 * log_var)
        eps = torch.randn_like(std)
        z = mu + eps * std
        
        return z, mu, log_var

class VAE(nn.Module):
    def __init__(self, encoder1, encoder2, decoder):
        super(VAE, self).__init__()
        self.encoder1 = encoder1
        self.encoder2 = encoder2
        self.decoder = decoder
    
    def forward(self, x, edge_index):
        z1, mu1, log_var1 = self.encoder1(x, edge_index)
        z2, mu2, log_var2 = self.encoder2(x)
        z = torch.cat([z1, z2], dim=-1)
        x_hat = self.decoder(z)
        return x_hat, mu1, log_var1, mu2, log_var2

class Decoder(nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels, num_layers):
        super(Decoder, self).__init__()
        self.num_layers = num_layers
        self.layers = nn.ModuleList()
        
        for i in range(num_layers):
            self.layers.append(
                nn.Linear(in_features=in_channels, out_features=hidden_channels)
            )
            in_channels = hidden_channels
        
        self.out = nn.Linear(in_channels, out_channels)
    
    def forward(self, x):
        for i in range(self.num_layers):
            x = self.layers[i](x)
            x = F.elu(x)
        
        x = self.out(x)
        x = torch.sigmoid(x)
        
        return x

class MyDataset(torch.utils.data.Dataset):
    def __init__(self, data):
        super(MyDataset, self).__init__()
        self.data = data
    
    def __getitem__(self, index):
        x = self.data[index]
        return x
    
    def __len__(self):
        return len(self.data)

def collate_fn(batch):
    return torch.stack(batch)

# Hyperparameters
num_layers = 3
hidden_channels = 64
out_channels = 32
latent_channels = 16
lr = 0.001
num_epochs = 50
batch_size = 32

# Load data
data = torch.randn(7, 1358, 12, 307, 2)
data = data.permute(1, 0, 2, 3, 4)
edge_index = ...  # construct adjacency matrix

# Create model
encoder1 = Encoder(in_channels=2, out_channels=latent_channels, hidden_channels=hidden_channels, num_layers=num_layers)
encoder2 = MLP(in_channels=2, out_channels=latent_channels, hidden_channels=hidden_channels, num_layers=num_layers)
decoder = Decoder(in_channels=2 * latent_channels, hidden_channels=hidden_channels, out_channels=2, num_layers=num_layers)
model = VAE(encoder1=encoder1, encoder2=encoder2, decoder=decoder)

# Set up optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=lr)

# Set up data loader
dataset = MyDataset(data)
loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn)

# Train the model
for epoch in range(num_epochs):
    for batch in loader:
        optimizer.zero_grad()
        x = batch.float()
        x_hat, mu1, log_var1, mu2, log_var2 = model(x, edge_index)
        loss = ...  # compute the total loss
        loss.backward()
        optimizer.step()
    
    print(f"Epoch {epoch + 1}, loss = {loss.item():.4f}")

关于如何避免内存爆炸和被杀的问题，可以考虑一些方法：

- 减小 batch size；
- 使用更小的模型或层数；
- 减小输入数据的维度；
- 使用数据增强等技术；
- 调整超参数，如学习率等。

另外，计算度相同的节点之间的注意力系数，可以考虑在 GATConv 中自定义一个 agg 函数，对邻居节点进行聚合并计算注意力系数。具体实现可以参考 PyG 中 GATConv 的源代码。最后，为了使两个编码器的隐变量拼接或相加起来，需要保证它们的输出维度相同，可以通过调整 MLP 和 GAT 的超参数来实现。