code: def test_is_pickleable(self, obs_dim, action_dim):
      env = GymEnv(
          DummyDiscreteEnv(obs_dim=obs_dim, action_dim=action_dim)
      )
      qf = DiscreteMLPDuelingQFunction(env_spec=env.spec)
      env.reset()
      obs = env.step(1).observation
  
      with tf.compat.v1.variable_scope(
          "DiscreteMLPDuelingQFunction", reuse=True
      ):
          bias = tf.compat.v1.get_variable("state_value/hidden_0/bias")
      # assign it to all one
      bias.load(tf.ones_like(bias).eval())
  
      output1 = self.sess.run(qf.q_vals, feed_dict={qf.input: [obs]})
  
      h_data = pickle.dumps(qf)
      with tf.compat.v1.Session(graph=tf.Graph()) as sess:
          qf_pickled = pickle.loads(h_data)
          output2 = sess.run(
              qf_pickled.q_vals, feed_dict={qf_pickled.input: [obs]}
          )
  
      assert np.array_equal(output1, output2)
  
file_path: garage/tests/garage/tf/q_functions/test_discrete_mlp_dueling_q_function.py
imports: import pickle
  import numpy
  import pytest
  import tensorflow
  from garage.envs import GymEnv
  from garage.tf.q_functions import DiscreteMLPDuelingQFunction
  from tests.fixtures import TfGraphTestCase
  from tests.fixtures.envs.dummy import DummyDiscreteEnv
label_desc: Use image node_id 4 for calling the TestDiscreteMLPDuelingQFunction obj's underlying member method code with example usage: obj.test_is_pickleable(obs_dim, action_dim) without return types