code: def __init__(
      self, groups=16, eps=2e-5, mean=None, projection=None, decay=0.9
  ):
      self.groups = groups
  
      self.running_mean = mean
      self.running_projection = projection
  
      self.eps = eps
      self.decay = decay
      self.axis = None
  
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 1 to create a new DecorrelatedBatchNormalization object from inherited base classes: function_node with example: obj = DecorrelatedBatchNormalization(groups, eps, mean, projection, decay)