class GAT(nn.Module): def __init__(self, in_dim, out_dim, num_heads): super(GAT, self).__init__() self.num_heads = num_heads self.attentions = nn.ModuleList([nn.Linear(in_dim, out_dim) for _ in range(num_heads)]) self.out_att = nn.Linear(in_dim*num_heads, out_dim) def forward(self, x, adj): x = x.unsqueeze(1) x = x.transpose(2,0) x = torch.cat([att(x) for att in self.attentions], dim=1) alpha = F.softmax(torch.matmul(x, x.transpose(1, 2)) / self.num_heads, dim=-1) alpha = torch.where(alpha>0, alpha, torch.zeros_like(alpha)) # alpha = torch.where(adj.unsqueeze(-1).bool(), alpha, torch.zeros_like(alpha)) alpha = alpha / alpha.sum(dim=-2, keepdim=True) out = torch.matmul(alpha, x).squeeze(1) out = F.elu(self.out_att(out)) return out 这段代码中out的形状为（192，512），而self.out_att只能接受（128，512）的输入，这段代码应该怎么调整呢。我尝试在self部分增加一个线性全连接层linear（512，128），但是报错缺少必要的位置参数，我应该怎么办呢。这是pytorch版本

可以在 `forward` 函数中增加一个线性全连接层，将 `out` 的形状从（192，512）变为（192，128），代码如下：

class GAT(nn.Module):
    def __init__(self, in_dim, out_dim, num_heads):
        super(GAT, self).__init__()
        self.num_heads = num_heads
        self.attentions = nn.ModuleList([nn.Linear(in_dim, out_dim) for _ in range(num_heads)])
        self.out_att = nn.Linear(in_dim*num_heads, out_dim)
        self.linear = nn.Linear(512, 128) # 新增的全连接层

    def forward(self, x, adj):
        x = x.unsqueeze(1)
        x = x.transpose(2,0)
        x = torch.cat([att(x) for att in self.attentions], dim=1)
        alpha = F.softmax(torch.matmul(x, x.transpose(1, 2)) / self.num_heads, dim=-1)
        alpha = torch.where(alpha>0, alpha, torch.zeros_like(alpha))
        # alpha = torch.where(adj.unsqueeze(-1).bool(), alpha, torch.zeros_like(alpha))
        alpha = alpha / alpha.sum(dim=-2, keepdim=True)
        out = torch.matmul(alpha, x).squeeze(1)
        out = F.elu(self.out_att(out))
        out = self.linear(out) # 新增的全连接层
        return out

关于报错缺少必要的位置参数，可以检查一下代码中是否存在遗漏的参数或者参数位置错误的情况。如果还有问题可以提供更详细的错误信息以及代码段。