code: def numerical_gradients_2d(input_, cuda=False):
      """
      numerical gradients implementation over batches using torch group conv operator.
      the single sided differences are re-computed later.
      it matches np.gradient(image) with the difference than here output=x,y for an image while there output=y,x
      :param input_: N,C,H,W
      :param cuda: whether or not use cuda
      :return: X,Y
      """
      n, c, h, w = input_.shape
      if not (h > 1 and w > 1):
          raise AssertionError
      x, y = gradient_central_diff(input_)
      return x, y
  
file_path: pywick/pywick/models/segmentation/testnets/gscnn/my_functionals/custom_functional.py
imports: import torch
  import torch.nn.functional
  from torchvision.transforms.functional import pad
  import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: numerical_gradients_2d(input_, cuda) and returns: x,  y