code: def test_sac_to_bc_networks(self):
      # Create a fake environment to test with.
      environment = fakes.ContinuousEnvironment(
          episode_length=10, bounded=True, action_dim=6
      )
      spec = specs.make_environment_spec(environment)
  
      sac_nets = sac.make_networks(spec, hidden_layer_sizes=(4, 4))
      bc_nets = bc.convert_to_bc_network(sac_nets.policy_network)
  
      rng = jax.random.PRNGKey(0)
      dummy_obs = utils.zeros_like(spec.observations)
      dummy_obs = utils.add_batch_dim(dummy_obs)
  
      sac_params = sac_nets.policy_network.init(rng)
      sac_output = sac_nets.policy_network.apply(sac_params, dummy_obs)
  
      bc_params = bc_nets.init(rng)
      bc_output = bc_nets.apply(
          bc_params, dummy_obs, is_training=False, key=None
      )
  
      np.testing.assert_array_equal(sac_output.mode(), bc_output.mode())
  
file_path: acme/acme/agents/jax/bc/pretraining_test.py
imports: from acme import specs
  from acme.agents.jax import bc
  from acme.agents.jax import sac
  from acme.jax import networks
  from acme.jax import utils
  from acme.testing import fakes
  import haiku
  import jax
  import numpy
  from absl.testing import absltest
label_desc: Use image node_id 2 for calling the BcPretrainingTest obj's underlying member method code with example usage: obj.test_sac_to_bc_networks() without return types