import tensorflow as tf

    observ_shape = self._parse_shape(self._batch_env.observation_space)
    print(observ_shape)
    observ_dtype = self._parse_dtype(self._batch_env.observation_space)
    print(observ_dtype)
    action_shape = self._parse_shape(self._batch_env.action_space)
    print(action_shape)
    action_dtype = self._parse_dtype(self._batch_env.action_space)
    print(action_dtype)
    with tf.variable_scope('env_temporary'):
      self._observ = tf.Variable(
          lambda: tf.zeros(batch_dims + observ_shape, observ_dtype),
          name='observ', trainable=False)
      self._action = tf.Variable(
          lambda: tf.zeros(batch_dims + action_shape, action_dtype),
          name='action', trainable=False)
      self._reward = tf.Variable(
          lambda: tf.zeros(batch_dims, tf.float32),
          name='reward', trainable=False)
      self._done = tf.Variable(
          lambda: tf.cast(tf.ones(batch_dims), tf.bool),
