diff --git a/test/test_nn.py b/test/test_nn.py
index 000e459164..72fc0d77df 100755
--- a/test/test_nn.py
+++ b/test/test_nn.py
@@ -13,7 +13,7 @@ from test.helpers import not_support_multi_device
 
 @unittest.skipIf(CI and Device.DEFAULT in {"CUDA", "NV"}, "slow")
 class TestNN(unittest.TestCase):
-  def test_sparse_cat_cross_entropy(self):
+  def test_sparse_categorical_crossentropy(self):
     # create in tinygrad
     input_tensor = Tensor.randn(6, 5) # not square to test that mean scaling uses the correct dimension
     target = Tensor([0, 0, 0, 1, 2, 3])  # torch doesn't support target=-1
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 40bd2e20af..55e8cf6735 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -3919,7 +3919,7 @@ class Tensor(MathTrait):
     ```
     """
     assert 0.0 <= label_smoothing <= 1.0, "label_smoothing must be in [0.0, 1.0]"
-    assert reduction in ("mean", "sum", "none"), "reduction must be one of ['mean', 'sum', 'none']"
+    assert reduction in get_args(ReductionStr), f"reduction must be one of {get_args(ReductionStr)}"
     log_probs, loss_mask = self.log_softmax(), (Y != ignore_index) if ignore_index != -1 else Y.ones_like(dtype=dtypes.bool)
     y_counted = Y.to(self.device).flatten().reshape(-1, 1)._one_hot_along_dim(self.shape[-1])
     y = (y_counted * loss_mask.reshape(-1, 1)).reshape(*Y.shape, self.shape[-1])