[fix] re-run black to fix CPU tests on master (#123)

fairscale/optim/oss.py

@@ -137,7 +137,7 @@ class OSS(Optimizer):
 
     @property
     def param_to_rank(self) -> Dict[torch.Tensor, int]:
-        '''param to data parallel rank'''
+        """param to data parallel rank"""
         if len(self._param_rank) == 0:
             for rank, param_groups in enumerate(self.partition_parameters()):
                 for param_group in param_groups:
@@ -145,11 +145,11 @@ class OSS(Optimizer):
                         self._param_rank[param] = rank
         return self._param_rank
 
-    def get_global_rank(self, group, rank):
+    def get_global_rank(self, group: Any, rank: int) -> int:
         if group is dist.group.WORLD:
             return rank
         else:
-            global_rank = dist.distributed_c10d._get_global_rank(group, rank)
+            global_rank = dist.distributed_c10d._get_global_rank(group, rank)  # type: ignore
         return global_rank
 
     # NOTE(msb) We add a kwargs in order to support Optimizer sub-classes that support extra kwargs.
@@ -376,7 +376,9 @@ class OSS(Optimizer):
                 logging.debug(
                     "Sending the sharded optimizer state to the reference replica from rank %s", rank,
                 )
-                broadcast_object(self.local_state_dict(), src_rank=self.global_rank, group=self.group, dist_device=self._device)
+                broadcast_object(
+                    self.local_state_dict(), src_rank=self.global_rank, group=self.group, dist_device=self._device
+                )
             else:
                 global_rank = self.get_global_rank(self.group, rank)
                 # Discard this tensor/rank, broadcast necessary for syncing
@@ -393,4 +395,3 @@ class OSS(Optimizer):
             for p in partition:
                 for t in p["params"]:
                     t.grad = None
-