code: def layer_normalization_backward(
      grad_inputs,
      inputs,
      input_shapes,
      outputs,
      output_shapes,
      batch_axis=(0,),
      eps=1e-05,
      no_scale=False,
      no_bias=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]
      x = inputs[0]
      g_idx = 1 if no_bias else 2
      if not no_scale:
          gamma = inputs[g_idx]
  
      batch_axis = tuple([b + x.ndim * (b < 0) for b in batch_axis])
  
      # w.r.t. x
      axes = list(set(range(x.ndim)) - set(batch_axis))
      dy_tn = dy * gamma if not no_scale else dy
      grads, xn = tensor_normalization_backward(
          [dy_tn], [x], [x.shape], [None], [None], axes, eps, True, True
      )
      dx = grads[0]
      res_grads = (dx,)
  
      # w.r.t. beta
      if not no_bias:
          db = F.sum(dy, axis=batch_axis, keepdims=True)
          res_grads += (db,)
  
      # w.r.t. gamma
      if not no_scale:
          dg = F.sum(dy * xn, axis=batch_axis, keepdims=True)
          res_grads += (dg,)
  
      return res_grads
  
label_desc: Use image node_id 1 for calling a global function with example usage: layer_normalization_backward(grad_inputs, inputs, input_shapes, outputs, output_shapes, batch_axis, eps, no_scale, no_bias) and returns: res_grads