[skip ci] adding type annotations

ml-agents/mlagents/trainers/cli_utils.py

         nargs="*",
         help="Absolute paths of plugins to be loaded",
         required=False,
-        action=DetectDefault
+        action=DetectDefault,
     )
     argparser.add_argument(
         "--env-args",

ml-agents/mlagents/trainers/learn.py

     return all_subclasses
 
 
-def get_initializer_trainer(paths) -> List:
+def get_initializer_trainer(paths: List[str]) -> List:
-    logger.info(f"Found {len(original_initializers)} initializers and {len(original_trainers)} "
-                f"trainers.")
+    logger.info(
+        f"Found {len(original_initializers)} initializers and {len(original_trainers)} "
+        f"trainers."
+    )
 
     # add all plugin paths to system path
     for p in paths:
         logger.info(f"Found {len(new_trainers)} new trainers")
         return new_trainers
     else:
-        raise ValueError("there should be exactly one initializer passed through plugins option")
+        raise ValueError(
+            "there should be exactly one initializer passed through plugins option"
+        )
 
 
 def get_version_string() -> str:
         )
 
         new_trainers = get_initializer_trainer(options.plugins)
+        print(new_trainers)
         trainer_factory = TrainerFactory(
             options.behaviors,
             write_path,

ml-agents/mlagents/trainers/settings.py

     # These are options that are relevant to the run itself, and not the engine or environment.
     # They will be left here.
     debug: bool = parser.get_default("debug")
-    plugins = parser.get_default("plugins")
+    plugins: List[str] = parser.get_default("plugins")
     # Strict conversion
     cattr.register_structure_hook(EnvironmentSettings, strict_to_cls)
     cattr.register_structure_hook(EngineSettings, strict_to_cls)

ml-agents/mlagents/trainers/trainer_util.py

 from mlagents.trainers.ghost.controller import GhostController
 from mlagents.trainers.settings import TrainerSettings, TrainerType
 from mlagents_distributed.trainers.ppo.trainer import DistributedPPOTrainer
-from mlagents_distributed.distributed_trainers.sac.distributed_sac_trainer import \
-    DistributedSACTrainer
+from mlagents_distributed.distributed_trainers.sac.distributed_sac_trainer import (
+    DistributedSACTrainer,
+)
 
 logger = get_logger(__name__)