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, gamma, beta = inputs
      xp = cuda.cupy
  
      interm_dtype = numpy.promote_types(x.dtype, gamma.dtype)
  
      gamma = gamma.astype(interm_dtype, copy=False)
      beta = beta.astype(interm_dtype, copy=False)
  
      orig_shape = x.shape
      batch_size, channels = orig_shape[:2]
      groups = self.groups
      cudnn_shape = (1, batch_size * groups, -1, 1)
      x = x.reshape(cudnn_shape)
  
      with x.device:
          dummy_beta = xp.zeros(batch_size * groups, dtype=beta.dtype)
          self.dummy_gamma = xp.ones_like(dummy_beta)
      (
          x_hat,
          self.mean,
          self.inv_std,
      ) = cudnn.batch_normalization_forward_training(
          x,
          self.dummy_gamma,
          dummy_beta,
          dummy_beta,
          dummy_beta,
          None,
          None,
          self.eps,
          1.0,
          True,
          libcudnn.CUDNN_BATCHNORM_SPATIAL,
          configuration.config.debug,
      )
  
      y = x_hat.reshape((batch_size, channels, -1))
      cuda.elementwise(
          "T gamma, T beta",
          "U y",
          "y = y * gamma + beta",
          "groupnorm_y",
      )(gamma[:, None], beta[:, None], y)
  
      y = y.reshape(orig_shape)
      return (y,)
  
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 4 for calling the GroupNormalization obj's underlying member method code with example usage: obj.forward_cudnn(inputs) and returns: y