code: def standardize(self, x):
          
          if self.preprocessing_function:
              x = self.preprocessing_function(x)
          if self.rescale:
              x *= self.rescale
          if self.samplewise_center:
              x -= np.mean(x, keepdims=True)
          if self.samplewise_std_normalization:
              x /= np.std(x, keepdims=True) + 1e-7
  
          if self.featurewise_center:
              if self.mean is not None:
                  x -= self.mean
              else:
                  warnings.warn('This ImageDataGenerator specifies '
                                '`featurewise_center`, but it hasn\'t '
                                'been fit on any training data. Fit it '
                                'first by calling `.fit(numpy_data)`.')
          if self.featurewise_std_normalization:
              if self.std is not None:
                  x /= (self.std + 1e-7)
              else:
                  warnings.warn('This ImageDataGenerator specifies '
                                '`featurewise_std_normalization`, but it hasn\'t '
                                'been fit on any training data. Fit it '
                                'first by calling `.fit(numpy_data)`.')
          if self.zca_whitening:
              if self.principal_components is not None:
                  flatx = np.reshape(x, (-1, np.prod(x.shape[-3:])))
                  whitex = np.dot(flatx, self.principal_components)
                  x = np.reshape(whitex, x.shape)
              else:
                  warnings.warn('This ImageDataGenerator specifies '
                                '`zca_whitening`, but it hasn\'t '
                                'been fit on any training data. Fit it '
                                'first by calling `.fit(numpy_data)`.')
          return x
docs: Apply the normalization configuration to a batch of inputs.
  
          # Arguments
              x: batch of inputs to be normalized.
  
          # Returns
              The inputs, normalized.