<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def _value_loss(self, observ, reward, length):
 """Compute the loss function for the value baseline. The value loss is the difference between empirical and approximated returns over the collected episodes. Returns the loss tensor and a summary strin. Args: observ: Sequences of observations. reward: Sequences of reward. length: Batch of sequence lengths. Returns: Tuple of loss tensor and summary tensor. """

    with tf.name_scope('value_loss'):
      value = self._network(observ, length).value
      return_ = utility.discounted_return(
          reward, length, self._config.discount)
      advantage = return_ - value
      value_loss = 0.5 * self._mask(advantage ** 2, length)
      summary = tf.summary.merge([
          tf.summary.histogram('value_loss', value_loss),
          tf.summary.scalar('avg_value_loss', tf.reduce_mean(value_loss))])
      value_loss = tf.reduce_mean(value_loss)
      return tf.check_numerics(value_loss, 'value_loss'), summary