code: def sobel_kernel(self):
      kernel_x = paddle.to_tensor(
          [[-1.0, 0.0, 1.0], [-2.0, 0.0, 2.0], [-1.0, 0.0, 1.0]]
      ).astype("float32")
      kernel_x = kernel_x / kernel_x.abs().sum()
      kernel_y = kernel_x.transpose([1, 0])
      kernel_x = kernel_x.unsqueeze(0).unsqueeze(0)
      kernel_y = kernel_y.unsqueeze(0).unsqueeze(0)
      kernel_x.stop_gradient = True
      kernel_y.stop_gradient = True
      return kernel_x, kernel_y
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  import cv2
label_desc: Use image node_id 4 for calling the GradientLoss obj's underlying member method code with example usage: obj.sobel_kernel() and returns: kernel_x,  kernel_y