code: def weight_standardization_backward(
      grad_inputs,
      inputs,
      input_shapes,
      outputs,
      output_shapes,
      channel_axis=None,
      eps=1e-05,
  ):
      """
      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 += grad_inputs[0].ndim * (channel_axis < 0)
      axes = list(set(range(x.ndim)) - set([channel_axis]))
      no_scale = True
      no_bias = True
      dx, _ = tensor_normalization_backward(
          grad_inputs,
          inputs,
          input_shapes,
          outputs,
          output_shapes,
          axes,
          eps,
          no_scale,
          no_bias,
      )
      return dx
  
label_desc: Use image node_id 1 for calling a global function with example usage: weight_standardization_backward(grad_inputs, inputs, input_shapes, outputs, output_shapes, channel_axis, eps) and returns: dx