code: def update(self, x):
      if x.ndim == len(self.shape):
          x = x.reshape(-1, *self.shape)
      assert x.shape[1:] == self.shape
  
      self._count += x.shape[0]
      self._sum += np.sum(x, axis=0)
      self._sumsq += np.sum(np.square(x), axis=0)
  
      self.mean = self._sum / float(self._count)
      self.std = np.sqrt(
          np.maximum(
              np.square(self.eps),
              self._sumsq / float(self._count) - np.square(self.mean),
          )
      )
  
imports: import numpy
  from keras.models import model_from_config, Sequential, Model, model_from_config
  import keras.optimizers
  import keras.backend
label_desc: Use image node_id 4 for calling the WhiteningNormalizer obj's underlying member method code with example usage: obj.update(x) without return types