code: def huber_loss_backward(
      grad_inputs,
      inputs,
      input_shapes,
      outputs,
      output_shapes,
      delta=1.0,
  ):
      """
      Args:
        grad_inputs (list of :obj:`nnabla.Variable`): Propagated grads to this backward function.
        inputs (list of :obj:`nnabla.Variable` and None): Input Variables of the forward function
            if this backward function depends on it. Otherwise, None is set instead.
        input_shapes (list of tuple of :obj:`int`): Input shapes of the forward function.
            The shapes of the inputs in which None is set can be passed.
        outputs (list of :obj:`nnabla.Variable` and None): Output Variables of the forward function
            if this backward function depends on it. Otherwise, None is set instead.
        output_shapes (list of tuple of :obj:`int`): Output shapes of the forward function.
            The shapes of the outputs in which None is set can be passed.
        kwargs (dict of arguments): Dictionary of the corresponding function arguments.
  
      Return:
        list of Variable: Return the gradients wrt inputs of the corresponding function.
      """
      dy = grad_inputs[0]
      x0 = inputs[0]
      x1 = inputs[1]
      d = x0 - x1
      m0 = F.less_scalar(F.abs(d), delta)
      m1 = 1 - m0
      mg = F.greater(x0, x1)
      ml = 1 - mg
      m0 = no_grad(m0)
      m1 = no_grad(m1)
      mg = no_grad(mg)
      ml = no_grad(ml)
      t0 = 2 * d * m0
      t1 = 2 * delta * m1 * mg
      t2 = -2 * delta * m1 * ml
      dx0 = dy * (t0 + t1 + t2)
      dx1 = -dx0
      return dx0, dx1
  
label_desc: Use image node_id 1 for calling a global function with example usage: huber_loss_backward(grad_inputs, inputs, input_shapes, outputs, output_shapes, delta) and returns: dx0,  dx1