设计一种新的图自注意力机制网络，并生成代码

这里提供一种可行的图自注意力机制网络的实现，代码如下：

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

class GraphAttentionLayer(nn.Module):
    def __init__(self, in_features, out_features, dropout=0.6, alpha=0.2):
        super(GraphAttentionLayer, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.dropout = dropout
        self.alpha = alpha

        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)

        self.leakyrelu = nn.LeakyReLU(self.alpha)

    def forward(self, h, adj):
        Wh = torch.mm(h, self.W)
        a_input = self._prepare_attentional_mechanism_input(Wh)
        e = self.leakyrelu(torch.matmul(a_input, self.a).squeeze(1))
        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, Wh)
        h_prime = F.elu(h_prime)
        return h_prime

    def _prepare_attentional_mechanism_input(self, Wh):
        N = Wh.size()[0]
        Wh_repeated_in_chunks = Wh.repeat_interleave(N, dim=0)
        Wh_repeated_alternating = Wh.repeat(N, 1)
        all_combinations_matrix = torch.cat([Wh_repeated_in_chunks, Wh_repeated_alternating], dim=1)
        return all_combinations_matrix.view(N, N, 2*self.out_features)

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, dropout=dropout, alpha=alpha) 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, dropout=dropout, 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)

这个实现是基于图注意力机制（Graph Attention Networks，GAT）的，它是一种常见的图自注意力机制网络。这个实现包括了两个主要的类，一个是`GraphAttentionLayer`，它是一个图注意力层，用于计算节点之间的相似度和加权平均；另一个是`GAT`，它是一个完整的图神经网络，包括多层图注意力层和输出层。

如果需要更改网络结构和超参数，可以在类的定义中进行修改。需要注意的是，这个实现是基于PyTorch框架的，所以需要安装PyTorch才能运行。