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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _std(self):
 """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. """

    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)