code: def decorrelated_batch_normalization(x, **kwargs):
      """decorrelated_batch_normalization(x, *, groups=16, eps=2e-5, \
  running_mean=None, running_projection=None, decay=0.9)
  
      Decorrelated batch normalization function.
  
      It takes the input variable ``x`` and normalizes it using
      batch statistics to make the output zero-mean and decorrelated.
  
      Args:
          x (:class:`~chainer.Variable`): Input variable.
          groups (int): Number of groups to use for group whitening.
          eps (float): Epsilon value for numerical stability.
          running_mean (:ref:`ndarray`): Expected value of the mean. This is a
              running average of the mean over several mini-batches using
              the decay parameter. If ``None``, the expected mean is initialized
              to zero.
          running_projection (:ref:`ndarray`):
              Expected value of the project matrix. This is a
              running average of the projection over several mini-batches using
              the decay parameter. If ``None``, the expected projected is
              initialized to the identity matrix.
          decay (float): Decay rate of moving average. It is used during
              training.
  
      Returns:
          ~chainer.Variable: The output variable which has the same shape as
          :math:`x`.
  
      See: `Decorrelated Batch Normalization <https://arxiv.org/abs/1804.08450>`_
  
      .. seealso:: :class:`~chainer.links.DecorrelatedBatchNormalization`
  
      """
      (
          groups,
          eps,
          running_mean,
          running_projection,
          decay,
      ) = argument.parse_kwargs(
          kwargs,
          ("groups", 16),
          ("eps", 2e-5),
          ("running_mean", None),
          ("running_projection", None),
          ("decay", 0.9),
      )
  
      f = DecorrelatedBatchNormalization(
          groups, eps, running_mean, running_projection, decay
      )
      return f.apply((x,))[0]
  
imports: import numpy
  from chainer import backend
  from chainer import function_node
  from chainer.utils import argument
  from chainer.utils import type_check
label_desc: Use image node_id 5 for calling a global function with example usage: decorrelated_batch_normalization(x) and returns: f