[CI] Fix PyLint errors. (#10837)

tests/python-gpu/test_from_cudf.py

@@ -195,7 +195,7 @@ class TestFromColumnar:
     @pytest.mark.skipif(**tm.no_cudf())
     def test_cudf_categorical(self) -> None:
         n_features = 30
-        _X, _y = tm.make_categorical(100, n_features, 17, False)
+        _X, _y = tm.make_categorical(100, n_features, 17, onehot=False)
         X = cudf.from_pandas(_X)
         y = cudf.from_pandas(_y)
 
@@ -312,7 +312,7 @@ class IterForDMatrixTest(xgb.core.DataIter):
             self._data = []
             self._labels = []
             for i in range(self.BATCHES):
-                X, y = tm.make_categorical(self.ROWS_PER_BATCH, 4, 13, False)
+                X, y = tm.make_categorical(self.ROWS_PER_BATCH, 4, 13, onehot=False)
                 self._data.append(cudf.from_pandas(X))
                 self._labels.append(y)
         else:

tests/python-gpu/test_gpu_prediction.py

@@ -405,7 +405,7 @@ class TestGPUPredict:
         )
 
     def test_shap_categorical(self):
-        X, y = tm.make_categorical(100, 20, 7, False)
+        X, y = tm.make_categorical(100, 20, 7, onehot=False)
         Xy = xgb.DMatrix(X, y, enable_categorical=True)
         booster = xgb.train(
             {"tree_method": "hist", "device": "gpu:0"}, Xy, num_boost_round=10

tests/python-gpu/test_gpu_updaters.py

@@ -140,7 +140,14 @@ class TestGPUUpdaters:
     @settings(deadline=None, max_examples=20, print_blob=True)
     @pytest.mark.skipif(**tm.no_pandas())
     def test_categorical_ohe(self, rows, cols, rounds, cats):
-        check_categorical_ohe(rows, cols, rounds, cats, "cuda", "hist")
+        check_categorical_ohe(
+            rows=rows,
+            cols=cols,
+            rounds=rounds,
+            cats=cats,
+            device="cuda",
+            tree_method="hist",
+        )
 
     @given(
         tm.categorical_dataset_strategy,
@@ -222,10 +229,9 @@ class TestGPUUpdaters:
     def test_categorical_32_cat(self):
         """32 hits the bound of integer bitset, so special test"""
         rows = 1000
-        cols = 10
-        cats = 32
-        rounds = 4
-        check_categorical_ohe(rows, cols, rounds, cats, "cuda", "hist")
+        check_categorical_ohe(
+            rows=rows, cols=10, rounds=4, cats=32, device="cuda", tree_method="hist"
+        )
 
     @pytest.mark.skipif(**tm.no_cupy())
     def test_invalid_category(self):