diff --git a/torch/nn/modules/batchnorm.py b/torch/nn/modules/batchnorm.py
index e76e307d36a6..48e58d637ea6 100644
--- a/torch/nn/modules/batchnorm.py
+++ b/torch/nn/modules/batchnorm.py
@@ -434,8 +434,14 @@ class SyncBatchNorm(_BatchNorm):
         >>> # With Learnable Parameters
         >>> m = nn.SyncBatchNorm(100)
         >>> # creating process group (optional)
-        >>> # process_ids is a list of int identifying rank ids.
-        >>> process_group = torch.distributed.new_group(process_ids)
+        >>> # ranks is a list of int identifying rank ids.
+        >>> ranks = list(range(8))
+        >>> r1, r2 = ranks[:4], ranks[4:] 
+        >>> # Note: every rank calls into new_group for every
+        >>> # process group created, even if that rank is not
+        >>> # part of the group.
+        >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
+        >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
         >>> # Without Learnable Parameters
         >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group)
         >>> input = torch.randn(20, 100, 35, 45, 10)
@@ -564,8 +570,14 @@ def convert_sync_batchnorm(cls, module, process_group=None):
             >>>            torch.nn.BatchNorm1d(100),
             >>>          ).cuda()
             >>> # creating process group (optional)
-            >>> # process_ids is a list of int identifying rank ids.
-            >>> process_group = torch.distributed.new_group(process_ids)
+            >>> # ranks is a list of int identifying rank ids.
+            >>> ranks = list(range(8))
+            >>> r1, r2 = ranks[:4], ranks[4:] 
+            >>> # Note: every rank calls into new_group for every
+            >>> # process group created, even if that rank is not
+            >>> # part of the group.
+            >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
+            >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
             >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group)
 
         """