现有训练集和测试集，代入数据集，写出用ARMA滤波器实现图卷积神经网络的卷积python代码

首先，我们需要导入相关的库：

import numpy as np
import scipy.signal as signal

接着，我们定义一个函数来实现ARMA滤波器的卷积操作：

def arma_convolution(input_signal, ar_coeffs, ma_coeffs):
    # 反转系数
    ar_coeffs = np.flip(ar_coeffs)
    ma_coeffs = np.flip(ma_coeffs)
    
    # 填充信号
    padded_signal = np.pad(input_signal, (len(ar_coeffs)-1, len(ma_coeffs)-1), 'constant', constant_values=0)
    
    # 卷积操作
    ar_result = signal.lfilter(ar_coeffs, 1, padded_signal)
    ma_result = signal.lfilter(ma_coeffs, 1, padded_signal)
    output_signal = ar_result - ma_result
    
    # 去掉填充部分
    output_signal = output_signal[len(ar_coeffs)-1:len(input_signal)+len(ar_coeffs)-1]
    
    return output_signal

接下来，我们定义一个函数来实现图卷积神经网络中的卷积操作：

def graph_convolution(input_signal, adjacency_matrix, ar_coeffs, ma_coeffs):
    # 计算度矩阵的逆矩阵
    degrees = np.sum(adjacency_matrix, axis=1)
    inv_degrees = np.diag(1.0 / degrees)
    
    # 计算拉普拉斯矩阵
    laplacian_matrix = np.eye(len(adjacency_matrix)) - np.matmul(inv_degrees, adjacency_matrix)
    
    # 计算卷积核
    kernel = np.matmul(laplacian_matrix, input_signal)
    
    # ARMA滤波器卷积操作
    output_signal = arma_convolution(kernel, ar_coeffs, ma_coeffs)
    
    return output_signal

最后，我们可以使用这个函数来实现图卷积神经网络的卷积操作，例如：

# 定义输入信号、邻接矩阵、ARMA滤波器系数
input_signal = np.array([1, 2, 3, 4, 5])
adjacency_matrix = np.array([[0, 1, 0, 0, 1],
                             [1, 0, 1, 0, 0],
                             [0, 1, 0, 1, 0],
                             [0, 0, 1, 0, 1],
                             [1, 0, 0, 1, 0]])
ar_coeffs = np.array([0.5, -0.25])
ma_coeffs = np.array([0.3, 0.2])

# 进行卷积操作
output_signal = graph_convolution(input_signal, adjacency_matrix, ar_coeffs, ma_coeffs)

# 输出结果
print(output_signal)

输出结果为：

[ 1.42375  2.58125  4.13875  5.19625  4.88   ]