code: def backward(self, indexes, grad_outputs):
      x, mean, projection = self.get_retained_inputs()
      (gy,) = grad_outputs
      f = FixedDecorrelatedBatchNormalizationGrad(self.groups)
      return f.apply((x, mean, projection, gy))
  
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 4 for calling the FixedDecorrelatedBatchNormalization obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: f