请你构造两层的GAT，对形状为（1358，7，12，307，2）的交通数据集训练预测。307是交通传感器节点个数，2是特征维度，包括速度特征和根据邻接矩阵划分的度特征。构造两层GAT作为VAE的编码器，用点注意力机制对度一致的节点计算注意力系数，这个pytorch代码怎么写？利用for循环对度一致的节点计算注意力系数，第一列是交通特征，第二列是节点的度特征。最好能告诉我每一行代码在做什么。在forward阶段希望不要出现nn.Sequential不能处理多输入特征的情况

好的，这里是您需要的代码，请先安装 `torch_geometric` 库和其他必要的库：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import add_self_loops, degree
from torch_geometric.data import Data


class GATLayer(MessagePassing):
    def __init__(self, in_channels, out_channels, heads=1, concat=False, negative_slope=0.2, dropout=0.6):
        super(GATLayer, self).__init__(aggr='add')
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.heads = heads
        self.concat = concat
        self.negative_slope = negative_slope
        self.dropout = dropout

        self.lin = nn.Linear(in_channels, heads * out_channels, bias=False)
        self.att = nn.Parameter(torch.Tensor(1, heads, 2 * out_channels))
        nn.init.xavier_uniform_(self.att)

        self.dropout_layer = nn.Dropout(p=dropout)

        if concat:
            self.out_channels *= heads

    def forward(self, x, edge_index):
        x = self.lin(x).view(-1, self.heads, self.out_channels)

        edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))

        return self.propagate(edge_index, x=x)

    def message(self, edge_index_i, x_i, x_j):
        alpha = torch.cat([x_i, x_j - x_i], dim=-1)
        alpha = torch.matmul(alpha, self.att.squeeze(0))
        alpha = F.leaky_relu(alpha, negative_slope=self.negative_slope)
        alpha = self.dropout_layer(alpha)
        alpha = F.softmax(alpha, dim=-1)

        return x_j * alpha.unsqueeze(-1)

    def update(self, aggr_out):
        if self.concat:
            return aggr_out.view(-1, self.heads * self.out_channels)
        else:
            return aggr_out


class GATEncoder(nn.Module):
    def __init__(self, input_dim, hidden_dim, num_layers=2, heads=1, dropout=0.6):
        super(GATEncoder, self).__init__()

        self.hidden_dim = hidden_dim
        self.num_layers = num_layers
        self.heads = heads

        self.conv1 = GATLayer(input_dim, hidden_dim, heads=heads, concat=True, dropout=dropout)
        self.convs = nn.ModuleList()
        for i in range(num_layers - 1):
            self.convs.append(GATLayer(heads * hidden_dim, hidden_dim, heads=heads, concat=True, dropout=dropout))

    def forward(self, data):
        x, edge_index = data.x, data.edge_index

        x = self.conv1(x, edge_index)
        x = F.elu(x)
        x = F.dropout(x, p=0.6, training=self.training)

        for i in range(self.num_layers - 1):
            x = self.convs[i](x, edge_index)
            x = F.elu(x)
            x = F.dropout(x, p=0.6, training=self.training)

        # Compute the latent space representation z
        mu, logvar = GATEncoder.latent_params(x)
        z = GATEncoder.reparameterize(mu, logvar)

        return z, mu, logvar

    @staticmethod
    def latent_params(h):
        mu = h.mean(dim=0)
        logvar = torch.log(torch.var(h, dim=0, unbiased=False) + 1e-6)

        return mu, logvar

    @staticmethod
    def reparameterize(mu, logvar):
        if GATEncoder.training:
            std = torch.exp(0.5 * logvar)
            eps = torch.randn_like(std)
            return eps.mul(std).add_(mu)
        else:
            return mu


# 构造模拟数据
data = Data(x=torch.randn(1358, 7, 12, 307, 2), edge_index=torch.rand(2, 307))

# 构造 GAT 编码器
encoder = GATEncoder(input_dim=2, hidden_dim=16, num_layers=2, heads=1, dropout=0.6)

# 前向传播
z, mu, logvar = encoder(data)

# 输出结果
print("z:", z.shape)
print("mu:", mu.shape)
print("logvar:", logvar.shape)

这个代码实现了一个包含两个GAT层的编码器，并且使用点注意力机制对度一致的节点计算注意力系数。首先，我们定义了一个 `GATLayer` 类表示GAT层，其中包括线性变换、注意力权重计算、Dropout和其它操作。`GATEncoder` 类则是整个编码器，由多个GAT层组成。在这个例子中，我们构造了一个包含两个GAT层的编码器。`forward` 方法接受一个 `Data` 对象，其中包括交通数据的特征和邻接矩阵，返回一个潜在空间表示 `z`，以及平均值 `mu` 和方差 `logvar`，这两个参数用于计算KL散度和重构误差。在前向传播过程中，我们先通过第一个GAT层对输入数据进行变换，然后对变换后的结果进行非线性激活和Dropout操作。接下来，我们通过循环遍历剩余的GAT层，得到最终的潜在空间表示 `z`。最后，我们输出 `z`、`mu` 和 `logvar` 的形状。