[Doc] Fix compact_graphs example docstring (#3953)

Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

python/dgl/transforms/functional.py

@@ -1994,8 +1994,8 @@ def compact_graphs(graphs, always_preserve=None, copy_ndata=True, copy_edata=Tru
     The following would compact the graph above to another bipartite graph with only
     two users and two games.
 
-    >>> new_g, induced_nodes = dgl.compact_graphs(g)
-    >>> induced_nodes
+    >>> new_g = dgl.compact_graphs(g)
+    >>> new_g.ndata[dgl.NID]
     {'user': tensor([1, 3]), 'game': tensor([3, 5])}
 
     The mapping tells us that only user #1 and #3 as well as game #3 and #5 are kept.
@@ -2013,8 +2013,8 @@ def compact_graphs(graphs, always_preserve=None, copy_ndata=True, copy_edata=Tru
 
     >>> g2 = dgl.heterograph({('user', 'plays', 'game'): ([1, 6], [6, 8])},
     >>>                      {'user': 20, 'game': 10})
-    >>> (new_g, new_g2), induced_nodes = dgl.compact_graphs([g, g2])
-    >>> induced_nodes
+    >>> new_g, new_g2 = dgl.compact_graphs([g, g2])
+    >>> new_g.ndata[dgl.NID]
     {'user': tensor([1, 3, 6]), 'game': tensor([3, 5, 6, 8])}
 
     Then one can see that user #1 from both graphs, users #3 from the first graph, as