code: def test_is_pickleable(self, obs_dim, action_dim):
      env = GymEnv(DummyBoxEnv(obs_dim=obs_dim, action_dim=action_dim))
      policy = GaussianMLPPolicy(env_spec=env.spec)
  
      obs = env.reset()[0]
  
      with tf.compat.v1.variable_scope("GaussianMLPPolicy", reuse=True):
          bias = tf.compat.v1.get_variable(
              "dist_params/mean_network/hidden_0/bias"
          )
      # assign it to all one
      bias.load(tf.ones_like(bias).eval())
  
      state_input = tf.compat.v1.placeholder(
          tf.float32, shape=(None, None, policy.input_dim)
      )
      dist_sym = policy.build(state_input, name="dist_sym").dist
      output1 = self.sess.run(
          [dist_sym.loc, dist_sym.stddev()],
          feed_dict={state_input: [[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.input_dim)
          )
          dist_sym = policy_pickled.build(
              state_input, name="dist_sym"
          ).dist
          output2 = sess.run(
              [dist_sym.loc, dist_sym.stddev()],
              feed_dict={state_input: [[obs.flatten()]]},
          )
          assert np.array_equal(output1, output2)
  
file_path: garage/tests/garage/tf/policies/test_gaussian_mlp_policy.py
imports: import pickle
  import numpy
  import pytest
  import tensorflow
  from garage.envs import GymEnv
  from garage.tf.policies import GaussianMLPPolicy
  from tests.fixtures import TfGraphTestCase
  from tests.fixtures.envs.dummy import DummyBoxEnv, DummyDictEnv, DummyDiscreteEnv
label_desc: Use image node_id 5 for calling the TestGaussianMLPPolicy obj's underlying member method code with example usage: obj.test_is_pickleable(obs_dim, action_dim) without return types