Remove return in test_lbfgs (#5875)

Similar to #5819, pytest functions should not `return` and seems unneeded in this test

Authors:
  - Matthew Roeschke (https://github.com/mroeschke)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: #5875

python/cuml/tests/dask/test_dask_logistic_regression.py

@@ -303,14 +303,7 @@ def assert_params(
     )
 
 
-@pytest.mark.mg
-@pytest.mark.parametrize("nrows", [1e5])
-@pytest.mark.parametrize("ncols", [20])
-@pytest.mark.parametrize("n_parts", [2, 23])
-@pytest.mark.parametrize("fit_intercept", [False, True])
-@pytest.mark.parametrize("datatype", [np.float32])
-@pytest.mark.parametrize("delayed", [True, False])
-def test_lbfgs(
+def _test_lbfgs(
     nrows,
     ncols,
     n_parts,
@@ -428,9 +421,25 @@ def array_to_numpy(ary):
     return lr
 
 
+@pytest.mark.mg
+@pytest.mark.parametrize("n_parts", [2, 23])
+@pytest.mark.parametrize("fit_intercept", [False, True])
+@pytest.mark.parametrize("delayed", [True, False])
+def test_lbfgs(n_parts, fit_intercept, delayed, client):
+    _test_lbfgs(
+        nrows=1e5,
+        ncols=20,
+        n_parts=n_parts,
+        fit_intercept=fit_intercept,
+        datatype=np.float32,
+        delayed=delayed,
+        client=client,
+    )
+
+
 @pytest.mark.parametrize("fit_intercept", [False, True])
 def test_noreg(fit_intercept, client):
-    lr = test_lbfgs(
+    lr = _test_lbfgs(
         nrows=1e5,
         ncols=20,
         n_parts=23,
@@ -494,7 +503,7 @@ def assert_small(X, y, n_classes):
 @pytest.mark.parametrize("n_classes", [8])
 def test_n_classes(n_parts, fit_intercept, n_classes, client):
     nrows = int(1e5) if n_classes < 5 else int(2e5)
-    lr = test_lbfgs(
+    lr = _test_lbfgs(
         nrows=nrows,
         ncols=20,
         n_parts=n_parts,
@@ -517,7 +526,7 @@ def test_n_classes(n_parts, fit_intercept, n_classes, client):
 @pytest.mark.parametrize("C", [1.0, 10.0])
 def test_l1(fit_intercept, datatype, delayed, n_classes, C, client):
     nrows = int(1e5) if n_classes < 5 else int(2e5)
-    lr = test_lbfgs(
+    lr = _test_lbfgs(
         nrows=nrows,
         ncols=20,
         n_parts=2,
@@ -545,7 +554,7 @@ def test_elasticnet(
     fit_intercept, datatype, delayed, n_classes, l1_ratio, client
 ):
     nrows = int(1e5) if n_classes < 5 else int(2e5)
-    lr = test_lbfgs(
+    lr = _test_lbfgs(
         nrows=nrows,
         ncols=20,
         n_parts=2,
@@ -585,7 +594,7 @@ def test_sparse_from_dense(
 
     nrows = int(1e5) if n_classes < 5 else int(2e5)
     run_test = partial(
-        test_lbfgs,
+        _test_lbfgs,
         nrows=nrows,
         ncols=20,
         n_parts=2,
@@ -699,7 +708,7 @@ def test_standardization_on_normal_dataset(
     nrows = int(1e5) if n_classes < 5 else int(2e5)
 
     # test correctness compared with scikit-learn
-    test_lbfgs(
+    _test_lbfgs(
         nrows=nrows,
         ncols=20,
         n_parts=2,