Remove some unneeded stuff

ml-agents/mlagents/trainers/tests/torch/test_policy.py

         NetworkSettings.MemorySettings() if use_rnn else None
     )
     policy = TorchPolicy(seed, mock_spec, trainer_settings)
-    return policy, trainer_settings
+    return policy
 
 
 @pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
     # Test evaluate
-    policy, _ = create_policy_mock(
+    policy = create_policy_mock(
         TrainerSettings(), use_rnn=rnn, use_discrete=discrete, use_visual=visual
     )
     decision_step, terminal_step = mb.create_steps_from_behavior_spec(
 @pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
 @pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
 def test_evaluate_actions(rnn, visual, discrete):
-    policy, trainer_settings = create_policy_mock(
+    policy = create_policy_mock(
         TrainerSettings(), use_rnn=rnn, use_discrete=discrete, use_visual=visual
     )
     buffer = mb.simulate_rollout(64, policy.behavior_spec, memory_size=policy.m_size)
 @pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
 @pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
 def test_sample_actions(rnn, visual, discrete):
-    policy, trainer_settings = create_policy_mock(
+    policy = create_policy_mock(
         TrainerSettings(), use_rnn=rnn, use_discrete=discrete, use_visual=visual
     )
     buffer = mb.simulate_rollout(64, policy.behavior_spec, memory_size=policy.m_size)