帮我用pytorch写一个图注意力网络的代码

好的，我可以回答这个问题。以下是一个使用 PyTorch 实现的图注意力网络的代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class GraphAttentionLayer(nn.Module):
    def __init__(self, in_features, out_features):
        super(GraphAttentionLayer, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.W = nn.Parameter(torch.zeros(size=(in_features, out_features)))
        nn.init.xavier_uniform_(self.W.data, gain=1.414)
        self.a = nn.Parameter(torch.zeros(size=(2*out_features, 1)))
        nn.init.xavier_uniform_(self.a.data, gain=1.414)

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

class GAT(nn.Module):
    def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads):
        super(GAT, self).__init__()
        self.dropout = dropout
        self.attentions = [GraphAttentionLayer(nfeat, nhid) for _ in range(nheads)]
        for i, attention in enumerate(self.attentions):
            self.add_module('attention_{}'.format(i), attention)
        self.out_att = GraphAttentionLayer(nhid*nheads, nclass)
        self.alpha = alpha

    def forward(self, x, adj):
        x = F.dropout(x, self.dropout, training=self.training)
        x = torch.cat([att(x, adj) for att in self.attentions], dim=1)
        x = F.dropout(x, self.dropout, training=self.training)
        x = F.elu(self.out_att(x, adj))
        return F.log_softmax(x, dim=1)

希望这个代码示例能够对你有所帮助。