code: def instance_normalization_backward(
      grad_inputs,
      inputs,
      input_shapes,
      outputs,
      output_shapes,
      channel_axis=None,
      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.
      """
      x = inputs[0]
  
      channel_axis += x.ndim * (channel_axis < 0)
      batch_axis = [b + x.ndim * (b < 0) for b in batch_axis]
  
      # Reduce [H, W]
      axes = list(set(range(x.ndim)) - set(batch_axis + [channel_axis]))
      grads, _ = tensor_normalization_backward(
          grad_inputs,
          inputs,
          input_shapes,
          [None],
          [None],
          axes,
          eps,
          no_scale,
          no_bias,
      )
      grads = list(grads)
  
      # Backward for scale/bias (gamma/beta) broadcasting
      for i in range(1, len(grads)):
          affine_param = inputs[i]  # gamma/beta
          affine_param_grad = grads[i]
          need_broadcast = False
          for ba in batch_axis:
              if affine_param.shape[ba] != affine_param_grad.shape[ba]:
                  need_broadcast = True
  
          if need_broadcast:
              grads[i] = F.sum(grads[i], axis=batch_axis, keepdims=True)
      return tuple(grads)
  
label_desc: Use image node_id 1 for calling a global function with example usage: instance_normalization_backward(grad_inputs, inputs, input_shapes, outputs, output_shapes, channel_axis, batch_axis, eps, no_scale, no_bias) and returns: tuple