make the code simpler.

python/dgl/utils/data.py

@@ -191,39 +191,28 @@ def graphdata2tensors(
                 data.format, tuple(F.tensor(a) for a in data.arrays)
             )
 
+    num_src, num_dst = None, None
     if isinstance(data, SparseAdjTuple):
         if idtype is not None:
             data = SparseAdjTuple(
                 data.format, tuple(F.astype(a, idtype) for a in data.arrays)
             )
-        num_src, num_dst = (
-            infer_num_nodes(data, bipartite=bipartite)
-            if infer_node_count
-            else (None, None)
-        )
+        if infer_node_count:
+            num_src, num_dst = infer_num_nodes(data, bipartite=bipartite)
     elif isinstance(data, list):
         src, dst = elist2tensor(data, idtype)
         data = SparseAdjTuple("coo", (src, dst))
-        num_src, num_dst = (
-            infer_num_nodes(data, bipartite=bipartite)
-            if infer_node_count
-            else (None, None)
-        )
+        if infer_node_count:
+            num_src, num_dst = infer_num_nodes(data, bipartite=bipartite)
     elif isinstance(data, sp.sparse.spmatrix):
         # We can get scipy matrix's number of rows and columns easily.
-        num_src, num_dst = (
-            infer_num_nodes(data, bipartite=bipartite)
-            if infer_node_count
-            else (None, None)
-        )
+        if infer_node_count:
+            num_src, num_dst = infer_num_nodes(data, bipartite=bipartite)
         data = scipy2tensor(data, idtype)
     elif isinstance(data, nx.Graph):
         # We can get networkx graph's number of sources and destinations easily.
-        num_src, num_dst = (
-            infer_num_nodes(data, bipartite=bipartite)
-            if infer_node_count
-            else (None, None)
-        )
+        if infer_node_count:
+            num_src, num_dst = infer_num_nodes(data, bipartite=bipartite)
         edge_id_attr_name = kwargs.get("edge_id_attr_name", None)
         if bipartite:
             top_map = kwargs.get("top_map")