code: def istft_backward(
      grad_inputs,
      inputs,
      input_shapes,
      outputs,
      output_shapes,
      window_size,
      stride,
      fft_size,
      window_type="hanning",
      center=True,
      pad_mode="reflect",
      as_stft_backward=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]
  
      dx_r, dx_i = F.stft(
          dy,
          window_size,
          stride,
          fft_size,
          window_type,
          center,
          pad_mode,
          as_istft_backward=not as_stft_backward,
      )
      return dx_r, dx_i
  
label_desc: Use image node_id 1 for calling a global function with example usage: istft_backward(grad_inputs, inputs, input_shapes, outputs, output_shapes, window_size, stride, fft_size, window_type, center, pad_mode, as_stft_backward) and returns: dx_r,  dx_i