diff --git a/test/python/fx_importer/sparse_test.py b/test/python/fx_importer/sparse_test.py
index 4cb372cea..95a859359 100644
--- a/test/python/fx_importer/sparse_test.py
+++ b/test/python/fx_importer/sparse_test.py
@@ -123,6 +123,11 @@ def sparse_export(
             # Zero preserving elt-wise unary op.
             if node.name in {"abs", "neg", "relu", "sin"}:
                 node.meta["sparsity"] = node.args[0].meta.get("sparsity", None)
+            elif node.name == "_to_sparse":
+                dim = len(node.meta.get("val").shape)
+                node.meta["sparsity"] = SparsityMeta(
+                    torch.sparse_coo, 0, dim, 0, None, torch.int64, torch.int64
+                )
     return prog
 
 
@@ -458,3 +463,51 @@ def test_sparse_coo3():
     print("torch.sparse")
     print(res1)
     print("torch.mlir")
+
+
+@run
+# CHECK-LABEL: test_sparse_activation
+# CHECK:       #[[$COO:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : compressed(nonunique), d1 : singleton(nonunique, soa), d2 : singleton(soa)), posWidth = 64, crdWidth = 64 }>
+# CHECK:       func.func @main(
+# CHECK-SAME:    %[[A:.*]]: !torch.vtensor<[2,2,2],f32>) -> !torch.vtensor<[2,2,2],f32,#[[$COO]]> {
+# CHECK:         %[[N1:.*]] = torch.constant.none
+# CHECK:         %[[N2:.*]] = torch.constant.none
+# CHECK:         %[[N3:.*]] = torch.constant.none
+# CHECK:         %[[R:.*]] = torch.operator "torch.aten._to_sparse"(%[[A]], %[[N1]], %[[N2]], %[[N3]]) : (!torch.vtensor<[2,2,2],f32>, !torch.none, !torch.none, !torch.none) -> !torch.vtensor<[2,2,2],f32,#[[$COO]]>
+# CHECK:         return %[[R]] : !torch.vtensor<[2,2,2],f32,#[[$COO]]>
+# CHECK:       }
+#
+# CHECK: torch.sparse
+# CHECK:   tensor(indices=tensor({{\[}}[0, 0, 0, 0, 1, 1, 1, 1],
+# CHECK:                               [0, 0, 1, 1, 0, 0, 1, 1],
+# CHECK:                               [0, 1, 0, 1, 0, 1, 0, 1]{{\]}}),
+# CHECK:      values=tensor([1., 1., 1., 1., 1., 1., 1., 1.]),
+# CHECK:      size=(2, 2, 2), nnz=8, layout=torch.sparse_coo)
+# CHECK: torch.mlir
+# CHECK:   [0 8]
+# CHECK:   [0 0 0 0 1 1 1 1]
+# CHECK:   [0 0 1 1 0 0 1 1]
+# CHECK:   [0 1 0 1 0 1 0 1]
+# CHECK:   [1. 1. 1. 1. 1. 1. 1. 1.]
+#
+def test_sparse_activation():
+    class SparseActivationCOO(torch.nn.Module):
+        def forward(self, x):
+            return x.to_sparse()
+
+    net = SparseActivationCOO()
+    x = torch.ones(2, 2, 2)
+    m = export_and_import(net, x)
+    print(m)
+
+    # Run it with PyTorch torch.sparse and with TORCH-MLIR sparse_jit.
+    res1 = net(x)
+    res2 = sparse_jit(net, x)
+    print("torch.sparse")
+    print(res1)
+    print("torch.mlir")
+    print(res2[0])
+    print(res2[1])
+    print(res2[2])
+    print(res2[3])
+    print(res2[4])