现有一个位置-结构-图卷积神经网络，对其加ARMA滤波器实现卷积，怎么写方法

假设你已经有了一个位置-结构-图卷积神经网络的代码，现在要对其加上ARMA滤波器实现卷积，可以按照以下步骤进行：

1. 导入所需的库

import torch
from torch_geometric.nn import ARMAConv

2. 定义ARMAConv卷积层

class ARMAConvNet(torch.nn.Module):
    def __init__(self, num_features, num_classes):
        super(ARMAConvNet, self).__init__()
        self.conv1 = ARMAConv(num_features, 16, 3, 2)
        self.conv2 = ARMAConv(16, 32, 3, 2)
        self.conv3 = ARMAConv(32, 64, 3, 2)
        self.fc1 = torch.nn.Linear(64, 128)
        self.fc2 = torch.nn.Linear(128, num_classes)

    def forward(self, x, edge_index, edge_attr):
        x = self.conv1(x, edge_index, edge_attr)
        x = torch.nn.functional.relu(x)
        x = self.conv2(x, edge_index, edge_attr)
        x = torch.nn.functional.relu(x)
        x = self.conv3(x, edge_index, edge_attr)
        x = torch.nn.functional.relu(x)
        x = torch.nn.functional.avg_pool2d(x, kernel_size=x.size()[2:])
        x = x.view(-1, 64)
        x = self.fc1(x)
        x = torch.nn.functional.relu(x)
        x = self.fc2(x)
        return x

其中，ARMAConv的参数依次为：输入特征数、输出特征数、滤波器大小、步长。

3. 训练模型

model = ARMAConvNet(num_features, num_classes)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
criterion = torch.nn.CrossEntropyLoss()

for epoch in range(num_epochs):
    model.train()
    optimizer.zero_grad()
    out = model(data.x, data.edge_index, data.edge_attr)
    loss = criterion(out, data.y)
    loss.backward()
    optimizer.step()

    model.eval()
    out = model(data.x, data.edge_index, data.edge_attr)
    pred = out.argmax(dim=1)
    acc = pred.eq(data.y).sum().item() / len(data.y)

    print(f'Epoch: {epoch}, Loss: {loss:.4f}, Accuracy: {acc:.4f}')

其中，data为输入数据，包括节点特征、边索引和边属性。

通过以上步骤，就可以将ARMA滤波器加入位置-结构-图卷积神经网络中实现卷积操作。