code: def pow2_quantize_backward(
      grad_inputs,
      inputs,
      input_shapes,
      outputs,
      output_shapes,
      sign=True,
      with_zero=True,
      n=8,
      m=1,
      ste_fine_grained=True,
  ):
      """
      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]
      raise NotImplementedError(
          "pow2_quantize_backward is not implemented."
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: pow2_quantize_backward(grad_inputs, inputs, input_shapes, outputs, output_shapes, sign, with_zero, n, m, ste_fine_grained) without return types