code: def test_is_pickleable(self):
      env = GymEnv(DummyDiscreteEnv(obs_dim=(1,), action_dim=1))
      policy = CategoricalLSTMPolicy(
          env_spec=env.spec, state_include_action=False
      )
  
      policy.reset()
      obs = env.reset()[0]
  
      state_input = tf.compat.v1.placeholder(
          tf.float32, shape=(None, None, policy.input_dim)
      )
      dist_sym = policy.build(state_input, name="dist_sym").dist
      policy._lstm_cell.weights[0].load(
          tf.ones_like(policy._lstm_cell.weights[0]).eval()
      )
  
      output1 = self.sess.run(
          [dist_sym.probs],
          feed_dict={state_input: [[obs.flatten()], [obs.flatten()]]},
      )
  
      p = pickle.dumps(policy)
  
      with tf.compat.v1.Session(graph=tf.Graph()) as sess:
          policy_pickled = pickle.loads(p)
          state_input = tf.compat.v1.placeholder(
              tf.float32, shape=(None, None, policy_pickled.input_dim)
          )
          dist_sym = policy_pickled.build(
              state_input, name="dist_sym"
          ).dist
          output2 = sess.run(
              [dist_sym.probs],
              feed_dict={
                  state_input: [[obs.flatten()], [obs.flatten()]]
              },
          )  # noqa: E126
          assert np.array_equal(output1, output2)
  
file_path: garage/tests/garage/tf/policies/test_categorical_lstm_policy.py
imports: import pickle
  import numpy
  import pytest
  import tensorflow
  from garage.envs import GymEnv
  from garage.tf.policies import CategoricalLSTMPolicy
  from tests.fixtures import TfGraphTestCase
  from tests.fixtures.envs.dummy import DummyBoxEnv, DummyDictEnv, DummyDiscreteEnv
label_desc: Use image node_id 6 for calling the TestCategoricalLSTMPolicy obj's underlying member method code with example usage: obj.test_is_pickleable() without return types