diff --git a/tests/test_operators.py b/tests/test_operators.py
index 35c0791..ccfc17b 100644
--- a/tests/test_operators.py
+++ b/tests/test_operators.py
@@ -17,9 +17,9 @@ def func_scalar(x):
 
 
 data = torch.rand((20, 3))
-inp = LabelTensor(data, ['x', 'y', 'mu']).requires_grad_(True)
+inp = LabelTensor(data, ['x', 'y', 'z']).requires_grad_(True)
 labels = ['a', 'b', 'c']
-tensor_v = LabelTensor(func_vec(inp), labels)
+tensor_v = LabelTensor(func_vector(inp), labels)
 tensor_s = LabelTensor(func_scalar(inp).reshape(-1, 1), labels[0])
 
 
@@ -107,6 +107,8 @@ def test_laplacian_scalar_output():
 
 def test_laplacian_vector_output():
     laplace_tensor_v = laplacian(tensor_v, inp)
+    print(laplace_tensor_v.labels)
+    print(tensor_v.labels)
     true_val = 2*torch.ones_like(tensor_v)
     assert laplace_tensor_v.shape == tensor_v.shape
     assert laplace_tensor_v.labels == [
@@ -124,3 +126,30 @@ def test_laplacian_vector_output():
         f'dd{i}' for i in ['a', 'b']
     ]
     assert torch.allclose(laplace_tensor_v, true_val)
+
+def test_laplacian_vector_output2():
+    x = LabelTensor(torch.linspace(0,1,10, requires_grad=True).reshape(-1,1), labels = ['x'])
+    y = LabelTensor(torch.linspace(3,4,10, requires_grad=True).reshape(-1,1), labels = ['y'])
+    input_ = LabelTensor(torch.cat((x,y), dim = 1), labels = ['x', 'y'])
+
+    # Construct two scalar functions:
+    # u = x**2 + y**2
+    # v = x**2 - y**2
+    u = LabelTensor(input_.extract('x')**2 + input_.extract('y')**2, labels='u')
+    v = LabelTensor(input_.extract('x')**2 - input_.extract('y')**2, labels='v')
+
+    # Define a vector-valued function, whose components are u and v.
+    f = LabelTensor(torch.cat((u,v), dim = 1), labels = ['u', 'v'])
+
+    # Compute the scalar laplacian of both u and v:
+    # Lap(u) = [4, 4, 4, ..., 4]
+    # Lap(v) = [0, 0, 0, ..., 0]
+    lap_u = laplacian(u, input_, components=['u'])
+    lap_v = laplacian(v, input_, components=['v'])
+
+    # Compute the laplacian of f: the two columns should correspond
+    # to the laplacians of u and v, respectively...
+    lap_f = laplacian(f, input_, components=['u', 'v'])
+
+    assert torch.allclose(lap_f.extract('ddu'), lap_u)
+    assert torch.allclose(lap_f.extract('ddv'), lap_v)