code: def _std(self):
      
      variance = tf.cond(
          self._count > 1,
          lambda: self._var_sum / tf.cast(self._count - 1, tf.float32),
          lambda: tf.ones_like(self._var_sum) * float('nan'))
      # The epsilon corrects for small negative variance values caused by
      # the algorithm. It was empirically chosen to work with all environments
      # tested.
      return tf.sqrt(variance + 1e-4)
docs: Computes the current estimate of the standard deviation.
  
      Note that the standard deviation is not defined until at least two samples
      were seen.
  
      Returns:
        Tensor of current variance.