diff --git a/test/python/fx_importer/sparse_test.py b/test/python/fx_importer/sparse_test.py
index 161b29148..d0b94ac83 100644
--- a/test/python/fx_importer/sparse_test.py
+++ b/test/python/fx_importer/sparse_test.py
@@ -272,6 +272,22 @@ def test_sparse_SpMM():
 # CHECK:         %[[R:.*]] = torch.aten.neg %arg0 : !torch.vtensor<[8,4,2],f32,#[[$CSRD]]> -> !torch.vtensor<[8,4,2],f32>
 # CHECK:         return %[[R]] : !torch.vtensor<[8,4,2],f32>
 # CHECK:       }
+#
+# CHECK:        torch.sparse
+# CHECK:        tensor(crow_indices=tensor([ 0,  4,  8, 12, 16, 20, 24, 28, 32]),
+# CHECK:        col_indices=tensor([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1,
+# CHECK:                            2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]),
+# CHECK:        values=tensor({{\[}}[ -1.,  -2.],
+# CHECK:                            [ -3.,  -4.],
+#                                   ...
+# CHECK:                            [-63., -64.]{{\]}}), size=(8, 4, 2), nnz=32,
+# CHECK:                       layout=torch.sparse_csr)
+# CHECK:        torch.mlir
+# CHECK:        {{\[\[}}[ -1.  -2.]
+# CHECK:                [ -3.  -4.]
+#                       ...
+# CHECK:                [-61. -62.]
+# CHECK:                [-63. -64.]{{\]\]}}
 def test_sparse_eltwise():
     class EltNet(torch.nn.Module):
         def __init__(self):
@@ -280,7 +296,9 @@ def test_sparse_eltwise():
         def forward(self, x):
             return -x
 
-    dense_input = torch.ones(8, 4, 2)
+    dense_input = torch.reshape(
+        torch.arange(1, 65, dtype=torch.float32), shape=(8, 4, 2)
+    )
 
     # This yields a **batched** CSR.
     sparse_input = dense_input.to_sparse_csr(dense_dim=0)
@@ -291,3 +309,17 @@ def test_sparse_eltwise():
     sparse_input = dense_input.to_sparse_csr(dense_dim=1)
     m = export_and_import(EltNet(), sparse_input)
     print(m)
+
+    # Run it with PyTorch torch.sparse and with TORCH-MLIR sparse_jit.
+    #
+    # TODO: note several issues that need to be fixed
+    #  (1) since we do not propagate sparsity into elt-wise, MLIR returns dense result
+    #  (2) for dense_dim=0, this will need a dense(batched) property
+    sparse_input = dense_input.to_sparse_csr(dense_dim=1)
+    net = EltNet()
+    res1 = net(sparse_input)
+    res2 = sparse_jit(net, sparse_input)
+    print("torch.sparse")
+    print(res1)
+    print("torch.mlir")
+    print(res2)