code: def log_det_jacobian(self, inputs):
      
      del inputs  # unused
      # Number of events is number of all elements excluding the batch and
      # channel dimensions.
      num_events = tf.reduce_prod(tf.shape(inputs)[1:-1])
      log_det_jacobian = num_events * tf.reduce_sum(self.log_scale)
      return log_det_jacobian
docs: Returns log det | dx / dy | = num_events * sum log | scale |.