code: def prod_backward(
      grad_inputs,
      inputs,
      input_shapes,
      outputs,
      output_shapes,
      axes=None,
      keep_dims=False,
  ):
      """
      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]
      axes = (
          [i for i in range(x0.ndim)]
          if axes is None
          else force_list(axes)
      )
      y0 = F.prod(x0, axes, keep_dims)
      if keep_dims:
          dx0 = F.broadcast(dy * y0 / x0, x0.shape)
      else:
          shape = [
              1 if i in axes else s for i, s in enumerate(x0.shape)
          ]
          dx0 = F.broadcast(F.reshape(dy * y0, shape) / x0, x0.shape)
      return dx0
  
label_desc: Use image node_id 1 for calling a global function with example usage: prod_backward(grad_inputs, inputs, input_shapes, outputs, output_shapes, axes, keep_dims) and returns: dx0