Creates ops for vector observation input.
        :param name: Name of the placeholder op.
        :param vec_obs_size: Size of stacked vector observation.
        :return:

def create_vector_input(self, name='vector_observation'):
        """
        Creates ops for vector observation input.
        :param name: Name of the placeholder op.
        :param vec_obs_size: Size of stacked vector observation.
        :return:
        """
        self.vector_in = tf.placeholder(shape=[None, self.vec_obs_size], dtype=tf.float32,
                                        name=name)
        if self.normalize:
            self.running_mean = tf.get_variable("running_mean", [self.vec_obs_size],
                                                trainable=False, dtype=tf.float32,
                                                initializer=tf.zeros_initializer())
            self.running_variance = tf.get_variable("running_variance", [self.vec_obs_size],
                                                    trainable=False,
                                                    dtype=tf.float32,
                                                    initializer=tf.ones_initializer())
            self.update_mean, self.update_variance = self.create_normalizer_update(self.vector_in)

            self.normalized_state = tf.clip_by_value((self.vector_in - self.running_mean) / tf.sqrt(
                self.running_variance / (tf.cast(self.global_step, tf.float32) + 1)), -5, 5,
                                                     name="normalized_state")
            return self.normalized_state
        else:
            return self.vector_in