code: def where_backward(
      grad_inputs, inputs, input_shapes, outputs, output_shapes
  ):
      """
      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]
      cd = inputs[0]
      xt_shape = input_shapes[1]
      xf_shape = input_shapes[2]
      c1 = F.constant(1, xt_shape)
      c0 = F.constant(0, xf_shape)
      m0 = F.where(cd, c1, c0)
      m1 = 1 - m0
      m0 = no_grad(m0)
      m1 = no_grad(m1)
      dx0 = dy * m0
      dx1 = dy * m1
      return None, dx0, dx1
  
label_desc: Use image node_id 1 for calling a global function with example usage: where_backward(grad_inputs, inputs, input_shapes, outputs, output_shapes) and returns: None,  dx0,  dx1