code: def backward(self, indexes, grad_outputs):
      (gy,) = grad_outputs
  
      f = DecorrelatedBatchNormalizationGrad(
          self.groups, self.eigvals, self.eigvectors, self.y_hat_pca
      )
      return f.apply((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 DecorrelatedBatchNormalization obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: f