code: def forward(self, inputs):
      xp = backend.get_array_module(*inputs)
      if (
          xp is cuda.cupy
          and chainer.should_use_cudnn(">=auto", 5000)
          and self.dummy_gamma is not None
      ):
          return self.forward_cudnn(inputs)
  
      self.retain_inputs((0, 1))
      _, gx_hat = inputs
      x_hat = self.x_hat
      self.x_hat = None
  
      gx_hat_avg = gx_hat.mean(axis=1, keepdims=True)
      gx_hat_x_hat_avg = (gx_hat * x_hat).mean(axis=1, keepdims=True)
      gx_std = gx_hat - gx_hat_avg - x_hat * gx_hat_x_hat_avg
      gx = self.inv_std[:, None] * gx_std
  
      self.retain_outputs((0,))
      return (gx,)
  
imports: import numpy
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  from chainer import configuration
  from chainer import function_node
  from chainer.utils import type_check
label_desc: Use image node_id 2 for calling the _XHatGrad obj's underlying member method code with example usage: obj.forward(inputs) and returns: gx, self