code: def mean_backward(
      grad_inputs,
      inputs,
      input_shapes,
      outputs,
      output_shapes,
      axes=None,
      keep_dims=False,
  ):
      dy = grad_inputs[0]
      x0_shape = input_shapes[0]
      x0_ndim = len(x0_shape)
      axes = (
          [i for i in range(x0_ndim)]
          if axes is None
          else force_list(axes)
      )
      n = np.prod(
          [s if i in axes else 1 for i, s in enumerate(x0_shape)]
      )
      if keep_dims:
          dx0 = F.broadcast(dy, x0_shape)
      else:
          shape = [
              1 if i in axes else s for i, s in enumerate(x0_shape)
          ]
          dx0 = F.broadcast(
              F.reshape(dy, shape, inplace=False), x0_shape
          )
      return dx0 / n
  
label_desc: Use image node_id 1 for calling a global function with example usage: mean_backward(grad_inputs, inputs, input_shapes, outputs, output_shapes, axes, keep_dims) and returns: unknown