Update kron tests to explicitly cover static and dynamic shapes

tests/tensor/test_nlinalg.py

@@ -732,36 +732,46 @@ def setup_method(self):
         super().setup_method()
 
     def test_vec_vec_kron_raises(self):
+        """Ensure kron raises an error for 1D inputs."""
         x = vector()
         y = vector()
         with pytest.raises(
             TypeError, match="kron: inputs dimensions must sum to 3 or more"
         ):
             kron(x, y)
 
+    @pytest.mark.parametrize("static_shape", [True, False])
     @pytest.mark.parametrize("shp0", [(2,), (2, 3), (2, 3, 4), (2, 3, 4, 5)])
     @pytest.mark.parametrize("shp1", [(6,), (6, 7), (6, 7, 8), (6, 7, 8, 9)])
-    def test_perform(self, shp0, shp1):
+    def test_perform(self, static_shape, shp0, shp1):
+        """Test kron execution and symbolic shape inference."""
         if len(shp0) + len(shp1) == 2:
             pytest.skip("Sum of shp0 and shp1 must be more than 2")
 
-        x = tensor(dtype="floatX", shape=shp0)
         a = np.asarray(self.rng.random(shp0)).astype(config.floatX)
-
-        y = tensor(dtype="floatX", shape=shp1)
         b = self.rng.random(shp1).astype(config.floatX)
 
+        # Using np.kron to evaluate expected numerical output and dimensionality
+        np_val = np.kron(a, b)
+
+        # Determine tensor shapes
+        shape_x = shp0 if static_shape else (None,) * len(shp0)
+        shape_y = shp1 if static_shape else (None,) * len(shp1)
+        shape_out = np_val.shape if static_shape else (None,) * np_val.ndim
+
+        x = tensor(dtype="floatX", shape=shape_x)
+        y = tensor(dtype="floatX", shape=shape_y)
+
         kron_xy = kron(x, y)
+
+        # Assert symbolic shape inference immediately after node creation
+        assert kron_xy.type.shape == shape_out
+
         f = function([x, y], kron_xy)
         out = f(a, b)
 
-        # Using np.kron to compare outputs
-        np_val = np.kron(a, b)
         np.testing.assert_allclose(out, np_val)
 
-        # Regression test for issue #1867
-        assert kron_xy.type.shape == np_val.shape
-
     @pytest.mark.parametrize(
         "i, shp0, shp1",
         [(0, (2, 3), (6, 7)), (1, (2, 3), (4, 3, 5)), (2, (2, 4, 3), (4, 3, 5))],