diff --git a/test/python/fx_importer/sparse_test.py b/test/python/fx_importer/sparse_test.py
index 680a52be558a..474fe2bfddbc 100644
--- a/test/python/fx_importer/sparse_test.py
+++ b/test/python/fx_importer/sparse_test.py
@@ -120,16 +120,18 @@ def sparse_export(
                     node.meta["sparsity"] = sparse_metadata(args[k])
                 k = k + 1
         elif node.op == "call_function":
+            # TODO: use upstream _opname implementation when available
+            opname = node.target._schema.name.split("::")[1]
             # Zero preserving elt-wise unary op.
-            if node.name in {"abs", "neg", "relu", "sin"}:
+            if opname in {"abs", "neg", "relu", "sin"}:
                 node.meta["sparsity"] = node.args[0].meta.get("sparsity", None)
-            elif node.name == "_to_sparse":
+            elif opname == "_to_sparse":
                 dim = len(node.meta.get("val").shape)
                 node.meta["sparsity"] = SparsityMeta(
                     torch.sparse_coo, 0, dim, 0, None, torch.int64, torch.int64
                 )
             # TODO: Uncomment this to hack sparsity into the network.
-            # elif node.name == "_to_dense":
+            # elif opname == "_to_dense":
             #     # hack (assumes we never really want the to_dense for now)
             #     node.meta["sparsity"] = node.args[0].meta.get("sparsity", None)
     return prog