code: def fixed_decorrelated_batch_normalization(
      x, mean, projection, groups=16
  ):
      """Decorrelated batch normalization function with fixed statistics.
  
      This is a variant of decorrelated batch normalization, where the mean and
      projection statistics are given by the caller as fixed variables. This is
      used in testing mode of the decorrelated batch normalization layer, where
      batch statistics cannot be used for prediction consistency.
  
      Args:
          x (:class:`~chainer.Variable`): Input variable.
          mean (:class:`~chainer.Variable` or :ref:`ndarray`):
              Shifting parameter of input.
          projection (:class:`~chainer.Variable` or :ref:`ndarray`):
              Projection matrix for decorrelation of input.
          groups (int): Number of groups to use for group whitening.
  
      Returns:
          ~chainer.Variable: The output variable which has the same shape as
          :math:`x`.
  
      .. seealso::
         :func:`~chainer.functions.decorrelated_batch_normalization`,
         :class:`~chainer.links.DecorrelatedBatchNormalization`
  
      """
      f = FixedDecorrelatedBatchNormalization(groups)
      return f.apply((x, mean, projection))[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 6 for calling a global function with example usage: fixed_decorrelated_batch_normalization(x, mean, projection, groups) and returns: f