renaming file from globals.py to global_values.py

ml-agents/mlagents/trainers/learn.py

     logger.info(f"The following plugins are available {discovered_plugins}")
 
     new_initializers = set(get_all_subclasses(Initializer))
-    if len(new_initializers) == 1:
+    if len(new_initializers) <= 0:
+        return
+    elif len(new_initializers) == 1:
         # load the initializer
         logger.info("Registering new initializer")
         distributed_init = list(new_initializers)[0]()

ml-agents/mlagents/trainers/policy/tf_policy.py

     GaussianDistribution,
     MultiCategoricalDistribution,
 )
-from mlagents.tf_utils.globals import get_rank
+from mlagents.tf_utils.global_values import get_rank
 
 
 logger = get_logger(__name__)

ml-agents/mlagents/trainers/saver/tf_saver.py

 from mlagents.trainers.policy.tf_policy import TFPolicy
 from mlagents.trainers.optimizer.tf_optimizer import TFOptimizer
 from mlagents.trainers import __version__
-from mlagents.tf_utils.globals import get_rank
+from mlagents.tf_utils import global_values
 
 
 logger = get_logger(__name__)
         self.graph = None
         self.sess = None
         self.tf_saver = None
-        self.rank = get_rank()
+        self.rank = global_values.get_rank()
 
     def register(self, module: Union[TFPolicy, TFOptimizer]) -> None:
         if isinstance(module, TFPolicy):
             self._load_graph(policy, self.model_path, reset_global_steps=reset_steps)
         else:
             policy.initialize()
-        TFPolicy.broadcast_global_variables(0)
+        TFPolicy.broadcast_global_variables
 
     def _load_graph(
         self, policy: TFPolicy, model_path: str, reset_global_steps: bool = False

ml-agents/mlagents/trainers/stats.py

 from mlagents_envs.logging_util import get_logger
 from mlagents_envs.timers import set_gauge
 from mlagents.tf_utils import tf, generate_session_config
-from mlagents.tf_utils.globals import get_rank
+from mlagents.tf_utils import global_values
 
 
 logger = get_logger(__name__)
         # If self-play, we want to print ELO as well as reward
         self.self_play = False
         self.self_play_team = -1
-        self.rank = get_rank()
+        self.rank = global_values.get_rank()
 
     def write_stats(
         self, category: str, values: Dict[str, StatsSummary], step: int

ml-agents/mlagents/trainers/trainer_controller.py

 from mlagents.trainers.trainer_util import TrainerFactory
 from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
 from mlagents.trainers.agent_processor import AgentManager
-from mlagents.tf_utils.globals import get_rank
+from mlagents.tf_utils import global_values
 
 
 class TrainerController:
         self.kill_trainers = False
         np.random.seed(training_seed)
         tf.set_random_seed(training_seed)
-        self.rank = get_rank()
+        self.rank = global_values.get_rank()
 
     @timed
     def _save_models(self):