code: def forward_cudnn(self, inputs):
      if self.eps < libcudnn.CUDNN_BN_MIN_EPSILON:
          raise RuntimeError(
              "cuDNN does not allow an eps value "
              "less than {}.".format(libcudnn.CUDNN_BN_MIN_EPSILON)
          )
  
      self.retain_inputs((0, 1))
      x, gx_hat = inputs
      self.x_hat = None
  
      # `x[None, :, :, None]` is slower because it results in a different
      # strides and cuDNN doesn't recognize it as a contiguous array.
      reduced_shape = x.shape
      cudnn_shape = (1,) + reduced_shape + (1,)
      x = x.reshape(cudnn_shape)
      gx_hat = gx_hat.reshape(cudnn_shape)
  
      gx, _, _ = cudnn.batch_normalization_backward(
          x,
          self.dummy_gamma,
          gx_hat,
          self.mean,
          self.inv_std,
          self.eps,
          True,
          libcudnn.CUDNN_BATCHNORM_SPATIAL,
          configuration.config.debug,
      )
      gx = gx.reshape(reduced_shape)
  
      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 3 for calling the _XHatGrad obj's underlying member method code with example usage: obj.forward_cudnn(inputs) and returns: gx