For fusion of 3 modalities about cen HOT 4 CLOSED

        if len(slim_params) % 3 == 0:
            slim_params.append(param[:len(param) // 3])
        elif len(slim_params) % 3 == 1:
            slim_params.append(param[len(param) // 3: len(param) // 3 * 2])
        else:
            slim_params.append(param[len(param) // 3 * 2:])

def forward(self, x, insnorm, insnorm_threshold):
    insnorm0, insnorm1, insnorm2 = insnorm[0].weight.abs(), insnorm[1].weight.abs(), insnorm[2].weight.abs()
    x0, x1, x2 = torch.zeros_like(x[0]), torch.zeros_like(x[1]), torch.zeros_like(x[2])
    x0[:, insnorm0 >= insnorm_threshold] = x[0][:, insnorm0 >= insnorm_threshold]
    x0[:, insnorm0 < insnorm_threshold] = (x[1][:, insnorm0 < insnorm_threshold] + x[2][:, insnorm0 < insnorm_threshold]) / 2
    x1[:, insnorm1 >= insnorm_threshold] = x[1][:, insnorm1 >= insnorm_threshold]
    x1[:, insnorm1 < insnorm_threshold] = (x[0][:, insnorm1 < insnorm_threshold] + x[2][:, insnorm1 < insnorm_threshold]) / 2
    x2[:, insnorm2 >= insnorm_threshold] = x[2][:, insnorm2 >= insnorm_threshold]
    x2[:, insnorm2 < insnorm_threshold] = (x[0][:, insnorm2 < insnorm_threshold] + x[1][:, insnorm2 < insnorm_threshold]) / 2
    return [x0, x1, x2]