Fix some mis-renamings

ml-agents/mlagents/trainers/demo_loader.py

                 )
             )
         # check observations match
-        if len(behavior_spec.observation_specs) != len(expected_behavior_spec.observation_specs):
+        if len(behavior_spec.observation_specs) != len(
+            expected_behavior_spec.observation_specs
+        ):
-                zip(behavior_spec.observation_specs, expected_behavior_spec.observation_specs)
+                zip(
+                    behavior_spec.observation_specs,
+                    expected_behavior_spec.observation_specs,
+                )
             ):
                 if demo_obs.shape != policy_obs.shape:
                     raise RuntimeError(

ml-agents/mlagents/trainers/tests/dummy_config.py

 def create_observation_specs_with_shapes(
     shapes: List[Tuple[int, ...]]
 ) -> List[ObservationSpec]:
-    obs_spec: List[ObservationSpec] = []
+    obs_specs: List[ObservationSpec] = []
-        obs_spec.append(spec)
-    return obs_spec
+        obs_specs.append(spec)
+    return obs_specs

ml-agents/mlagents/trainers/tests/mock_brain.py

 ) -> Tuple[DecisionSteps, TerminalSteps]:
     return create_mock_steps(
         num_agents=num_agents,
-        observation_spec=behavior_spec.observation_spec,
+        observation_specs=behavior_spec.observation_specs,
         action_spec=behavior_spec.action_spec,
     )
 
     else:
         action_spec = ActionSpec.create_continuous(vector_action_space)
     observation_shapes = [(84, 84, 3)] * int(use_visual) + [(vector_obs_space,)]
-    obs_spec = create_observation_spec_with_shapes(observation_shapes)
+    obs_spec = create_observation_specs_with_shapes(observation_shapes)
     behavior_spec = BehaviorSpec(obs_spec, action_spec)
     return behavior_spec
 

ml-agents/mlagents/trainers/tests/test_rl_trainer.py

     checkpoint_interval = trainer.trainer_settings.checkpoint_interval
     trajectory = mb.make_fake_trajectory(
         length=time_horizon,
-        observation_spec=create_observation_spec_with_shapes([(1,)]),
+        observation_spec=create_observation_specs_with_shapes([(1,)]),
         max_step_complete=True,
         action_spec=ActionSpec.create_discrete((2,)),
     )

ml-agents/mlagents/trainers/tests/test_trajectory.py

     wanted_keys = set(wanted_keys)
     trajectory = make_fake_trajectory(
         length=length,
-        observation_specs=create_observation_specs_with_shapes([(VEC_OBS_SIZE,), (84, 84, 3)]),
+        observation_specs=create_observation_specs_with_shapes(
+            [(VEC_OBS_SIZE,), (84, 84, 3)]
+        ),
         action_spec=ActionSpec.create_continuous(ACTION_SIZE),
     )
     agentbuffer = trajectory.to_agentbuffer()

ml-agents/mlagents/trainers/tests/torch/test_reward_providers/test_curiosity.py

 @pytest.mark.parametrize(
     "behavior_spec",
     [
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS),
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE
+        ),
     ],
 )
 def test_construction(behavior_spec: BehaviorSpec) -> None:
 @pytest.mark.parametrize(
     "behavior_spec",
     [
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS
+        ),
         BehaviorSpec(
             create_observation_specs_with_shapes([(10,), (64, 66, 3), (84, 86, 1)]),
             ACTIONSPEC_CONTINUOUS,
             ACTIONSPEC_TWODISCRETE,
         ),
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE
+        ),
     ],
 )
 def test_factory(behavior_spec: BehaviorSpec) -> None:
             create_observation_specs_with_shapes([(10,), (64, 66, 3), (24, 26, 1)]),
             ACTIONSPEC_CONTINUOUS,
         ),
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE),
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE
+        ),
     ],
 )
 def test_reward_decreases(behavior_spec: BehaviorSpec, seed: int) -> None:
 @pytest.mark.parametrize("seed", SEED)
 @pytest.mark.parametrize(
     "behavior_spec",
-    [BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS)],
+    [
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS
+        )
+    ],
 )
 def test_continuous_action_prediction(behavior_spec: BehaviorSpec, seed: int) -> None:
     np.random.seed(seed)
             create_observation_specs_with_shapes([(10,), (64, 66, 3), (24, 26, 1)]),
             ACTIONSPEC_CONTINUOUS,
         ),
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE),
-        BehaviorSpec(create_observation_spec_with_shapes([(10,)]), ACTIONSPEC_DISCRETE),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE
+        ),
     ],
 )
 def test_next_state_prediction(behavior_spec: BehaviorSpec, seed: int) -> None:

ml-agents/mlagents/trainers/tests/torch/test_reward_providers/test_extrinsic.py

 @pytest.mark.parametrize(
     "behavior_spec",
     [
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS),
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE
+        ),
     ],
 )
 def test_construction(behavior_spec: BehaviorSpec) -> None:
 @pytest.mark.parametrize(
     "behavior_spec",
     [
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS),
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE
+        ),
     ],
 )
 def test_factory(behavior_spec: BehaviorSpec) -> None:
 @pytest.mark.parametrize(
     "behavior_spec",
     [
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS),
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE
+        ),
     ],
 )
 def test_reward(behavior_spec: BehaviorSpec, reward: float) -> None:

ml-agents/mlagents/trainers/tests/torch/test_reward_providers/test_gail.py

     "behavior_spec",
     [
         BehaviorSpec(
-            create_observation_specs_with_shapes([(8,), (24, 26, 1)]), ACTIONSPEC_CONTINUOUS
+            create_observation_specs_with_shapes([(8,), (24, 26, 1)]),
+            ACTIONSPEC_CONTINUOUS,
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(50,)]), ACTIONSPEC_FOURDISCRETE
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE
-        BehaviorSpec(create_observation_specs_with_shapes([(50,)]), ACTIONSPEC_FOURDISCRETE),
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE),
     ],
 )
 @pytest.mark.parametrize("use_actions", [False, True])
     "behavior_spec",
     [
         BehaviorSpec(
-            create_observation_spec_with_shapes([(8,), (24, 26, 1)]), ACTIONSPEC_CONTINUOUS
+            create_observation_specs_with_shapes([(8,), (24, 26, 1)]),
+            ACTIONSPEC_CONTINUOUS,
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(50,)]), ACTIONSPEC_FOURDISCRETE
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE
-        BehaviorSpec(create_observation_spec_with_shapes([(50,)]), ACTIONSPEC_FOURDISCRETE),
-        BehaviorSpec(create_observation_spec_with_shapes([(10,)]), ACTIONSPEC_DISCRETE),
     ],
 )
 @pytest.mark.parametrize("use_actions", [False, True])

ml-agents/mlagents/trainers/tests/torch/test_reward_providers/test_rnd.py

 @pytest.mark.parametrize(
     "behavior_spec",
     [
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS),
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE
+        ),
     ],
 )
 def test_construction(behavior_spec: BehaviorSpec) -> None:
 @pytest.mark.parametrize(
     "behavior_spec",
     [
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_CONTINUOUS
+        ),
         BehaviorSpec(
             create_observation_specs_with_shapes([(10,), (64, 66, 3), (84, 86, 1)]),
             ACTIONSPEC_CONTINUOUS,
             ACTIONSPEC_TWODISCRETE,
         ),
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE
+        ),
     ],
 )
 def test_factory(behavior_spec: BehaviorSpec) -> None:
             create_observation_specs_with_shapes([(10,), (64, 66, 3), (24, 26, 1)]),
             ACTIONSPEC_CONTINUOUS,
         ),
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE),
-        BehaviorSpec(create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_TWODISCRETE
+        ),
+        BehaviorSpec(
+            create_observation_specs_with_shapes([(10,)]), ACTIONSPEC_DISCRETE
+        ),
     ],
 )
 def test_reward_decreases(behavior_spec: BehaviorSpec, seed: int) -> None:

ml-agents/mlagents/trainers/torch/components/reward_providers/curiosity_reward_provider.py

             vis_encode_type=EncoderType.SIMPLE,
             memory=None,
         )
-        self._state_encoder = NetworkBody(specs.observation_spec, state_encoder_settings)
+        self._state_encoder = NetworkBody(
+            specs.observation_spec, state_encoder_settings
+        )
 
         self._action_flattener = ActionFlattener(self._action_spec)
 

ml-agents/mlagents/trainers/torch/components/reward_providers/gail_reward_provider.py

         unencoded_size = (
             self._action_flattener.flattened_size + 1 if settings.use_actions else 0
         )  # +1 is for dones
-        self.encoder = NetworkBody(specs.observation_specs, encoder_settings, unencoded_size)
+        self.encoder = NetworkBody(
+            specs.observation_specs, encoder_settings, unencoded_size
+        )
 
         estimator_input_size = settings.encoding_size
         if settings.use_vail:

ml-agents/mlagents/trainers/torch/networks.py

     ):
         self.use_lstm = network_settings.memory is not None
         super().__init__(
-            observation_spec, network_settings, action_spec, conditional_sigma, tanh_squash
+            observation_spec,
+            network_settings,
+            action_spec,
+            conditional_sigma,
+            tanh_squash,
         )
         self.stream_names = stream_names
         self.value_heads = ValueHeads(stream_names, self.encoding_size)
     ):
         self.use_lstm = network_settings.memory is not None
         super().__init__(
-            observation_spec, network_settings, action_spec, conditional_sigma, tanh_squash
+            observation_spec,
+            network_settings,
+            action_spec,
+            conditional_sigma,
+            tanh_squash,
         )
         self.stream_names = stream_names
         self.critic = ValueNetwork(stream_names, observation_spec, network_settings)

ml-agents/tests/yamato/scripts/run_llapi.py

         print("Number of observations : ", len(group_spec.observation_specs))
 
         # Is there a visual observation ?
-        vis_obs = any(len(obs_spec.shape) == 3 for obs_spec in group_spec.observation_specs)
+        vis_obs = any(
+            len(obs_spec.shape) == 3 for obs_spec in group_spec.observation_specs
+        )
         print("Is there a visual observation ?", vis_obs)
 
         # Examine the state space for the first observation for the first agent