把一个卷积函数写成一个类

好的，以下是一个简单的卷积函数的类实现：

class Convolution:

    def __init__(self, kernel_size, stride):
        self.kernel_size = kernel_size
        self.stride = stride

    def forward(self, input_tensor, weight_tensor, bias_tensor):
        batch_size, in_channels, in_height, in_width = input_tensor.shape
        out_channels, _, kernel_height, kernel_width = weight_tensor.shape

        # 计算输出张量的大小
        out_height = int((in_height - kernel_height) / self.stride) + 1
        out_width = int((in_width - kernel_width) / self.stride) + 1

        # 初始化输出张量
        output_tensor = np.zeros((batch_size, out_channels, out_height, out_width))

        # 进行卷积计算
        for b in range(batch_size):
            for c_out in range(out_channels):
                for h_out in range(out_height):
                    for w_out in range(out_width):
                        # 计算输入张量中的每个窗口
                        h_start = h_out * self.stride
                        h_end = h_start + kernel_height
                        w_start = w_out * self.stride
                        w_end = w_start + kernel_width

                        window = input_tensor[b, :, h_start:h_end, w_start:w_end]

                        # 计算卷积结果
                        output_tensor[b, c_out, h_out, w_out] += np.sum(window * weight_tensor[c_out]) + bias_tensor[c_out]

        return output_tensor

这个类接收三个参数，分别为卷积核的大小、步长以及输入张量、权重张量和偏置张量。它实现了前向传递函数，计算输入张量和权重张量的卷积，并加上偏置张量，最后返回计算结果的输出张量。