code: def tf_loss(self, states, internals, reward, update, reference=None):
          
          prediction = self.predict(states=states, internals=internals, update=update)
          return tf.nn.l2_loss(t=(prediction - reward))
docs: Creates the TensorFlow operations for calculating the L2 loss between predicted
          state values and actual rewards.
  
          Args:
              states: Dict of state tensors.
              internals: List of prior internal state tensors.
              reward: Reward tensor.
              update: Boolean tensor indicating whether this call happens during an update.
              reference: Optional reference tensor(s), in case of a comparative loss.
  
          Returns:
              Loss tensor