请你仿造slot_attention代码，构造两层GAT对形状为（1358，7，12，307，2）的交通数据集计算隐变量。其中第2列是度特征，对度特征相同节点计算注意力系数，可能用到for循环。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并避免避免内存过大、邻接矩阵分配过大、killed等情况，是pytorch版本。

好的，这是一个相对复杂的问题，需要一步一步地来。

首先，我们要导入必要的库，包括PyTorch、numpy和scipy.sparse。同时，我们还需要定义一些超参数，包括节点特征的维度、注意力机制中的维度、头数和dropout率等。代码如下：

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from scipy.sparse import coo_matrix

class GAT(nn.Module):
    def __init__(self, in_dim, out_dim, num_heads, attn_dim, dropout):
        super(GAT, self).__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.num_heads = num_heads
        self.attn_dim = attn_dim
        self.dropout = dropout
        self.W = nn.Parameter(torch.Tensor(num_heads, in_dim, attn_dim))
        self.a = nn.Parameter(torch.Tensor(num_heads, 2 * attn_dim, 1))
        nn.init.xavier_uniform_(self.W)
        nn.init.xavier_uniform_(self.a)

    def forward(self, x, adj):
        x = F.dropout(x, self.dropout, training=self.training)
        h = torch.matmul(x, self.W)
        N = h.size()[0]
        a_input = torch.cat([h.repeat(1, 1, N).view(N * N, -1), h.repeat(1, N, 1)], dim=2).view(N, -1, 2 * self.attn_dim)
        e = F.leaky_relu(torch.matmul(a_input, self.a.repeat(N, 1, 1).view(N * N, -1, 1)).squeeze(2))
        zero_vec = -9e15 * torch.ones_like(e)
        attention = torch.where(adj > 0, e, zero_vec)
        attention = F.softmax(attention, dim=1)
        attention = F.dropout(attention, self.dropout, training=self.training)
        h_prime = torch.matmul(attention, h)
        return h_prime

接下来，我们需要构造一个图。由于我们的数据集比较大，我们采用稀疏矩阵的方式来存储图。我们先将邻接矩阵转换为COO格式，然后将其转换为稀疏矩阵。代码如下：

# 构造图
adj = np.zeros((1358, 1358))
# 假设数据集中第2列是度特征
degrees = data[:, :, :, :, 1].reshape((-1, 307))
for i in range(degrees.shape[0]):
    for j in range(degrees.shape[1]):
        adj[i, i+j*1358] = degrees[i, j]
# 将邻接矩阵转换为COO格式
adj_coo = coo_matrix(adj)
# 将COO格式转换为稀疏矩阵
adj = torch.sparse_coo_tensor((adj_coo.row, adj_coo.col), adj_coo.data, adj_coo.shape)

接下来，我们可以构造两层GAT模型。由于每个GAT模型都需要一个注意力机制，我们需要先构造一个注意力机制的类。代码如下：

class Attention(nn.Module):
    def __init__(self, in_dim, out_dim, num_heads, attn_dim, dropout):
        super(Attention, self).__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.num_heads = num_heads
        self.attn_dim = attn_dim
        self.dropout = dropout
        self.W = nn.Parameter(torch.Tensor(num_heads, in_dim, attn_dim))
        self.a = nn.Parameter(torch.Tensor(num_heads, 2 * attn_dim, 1))
        nn.init.xavier_uniform_(self.W)
        nn.init.xavier_uniform_(self.a)

    def forward(self, x):
        x = F.dropout(x, self.dropout, training=self.training)
        h = torch.matmul(x, self.W)
        N = h.size()[0]
        a_input = torch.cat([h.repeat(1, 1, N).view(N * N, -1), h.repeat(1, N, 1)], dim=2).view(N, -1, 2 * self.attn_dim)
        e = F.leaky_relu(torch.matmul(a_input, self.a.repeat(N, 1, 1).view(N * N, -1, 1)).squeeze(2))
        zero_vec = -9e15 * torch.ones_like(e)
        attention = torch.where(adj > 0, e, zero_vec)
        attention = F.softmax(attention, dim=1)
        attention = F.dropout(attention, self.dropout, training=self.training)
        h_prime = torch.matmul(attention, h)
        return h_prime

然后，我们就可以构造两层GAT模型了。代码如下：

class GAT(nn.Module):
    def __init__(self, in_dim, out_dim, num_heads, attn_dim, dropout):
        super(GAT, self).__init__()
        self.attentions = nn.ModuleList([Attention(in_dim, out_dim, num_heads, attn_dim, dropout) for _ in range(2)])
        self.out_layer = nn.Linear(out_dim * num_heads, out_dim)

    def forward(self, x, adj):
        x = torch.cat([att(x) for att in self.attentions], dim=2)
        x = self.out_layer(x)
        return x

gat = GAT(in_dim=307, out_dim=64, num_heads=4, attn_dim=64, dropout=0.1)
gat.to(device)
x = torch.randn(1358, 7, 12, 307, 2).to(device)
h = gat(x, adj)