现有一个class ARMAConv(nn.Module)，写代码调用这个函数

以下是一个简单的示例代码调用 ARMAConv 类:

import torch.nn as nn

class ARMAConv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
        super(ARMAConv, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
        
    def forward(self, x):
        x = self.conv(x)
        # add some ARMA operation here
        return x

# create an instance of ARMAConv
conv = ARMAConv(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1, dilation=1, groups=1, bias=True)

# pass some input tensor to the convolutional layer
input_tensor = torch.randn((1, 3, 32, 32))
output = conv(input_tensor)
print(output.shape)

上面的代码创建了一个 ARMAConv 实例，并将其应用于一个随机生成的输入张量。请注意，在 forward() 方法中，您需要添加一些 ARMA 操作。这里我只是简单地将输入张量通过卷积层传递，并返回输出张量。您需要根据您的需求修改 forward() 方法。