Fix pyspark parameter. (#9460)

- Don't pass the `use_gpu` parameter to the learner.
- Fix GPU approx with PySpark.

tests/test_distributed/test_gpu_with_spark/test_gpu_spark.py

@@ -151,12 +151,18 @@ def spark_diabetes_dataset_feature_cols(spark_session_with_gpu):
     return train_df, test_df, data.feature_names
 
 
-def test_sparkxgb_classifier_with_gpu(spark_iris_dataset):
+@pytest.mark.parametrize("tree_method", ["hist", "approx"])
+def test_sparkxgb_classifier_with_gpu(tree_method: str, spark_iris_dataset) -> None:
     from pyspark.ml.evaluation import MulticlassClassificationEvaluator
 
-    classifier = SparkXGBClassifier(device="cuda", num_workers=num_workers)
+    classifier = SparkXGBClassifier(
+        device="cuda", num_workers=num_workers, tree_method=tree_method
+    )
     train_df, test_df = spark_iris_dataset
     model = classifier.fit(train_df)
+    config = json.loads(model.get_booster().save_config())
+    ctx = config["learner"]["generic_param"]
+    assert ctx["device"] == "cuda:0"
     pred_result_df = model.transform(test_df)
     evaluator = MulticlassClassificationEvaluator(metricName="f1")
     f1 = evaluator.evaluate(pred_result_df)