diff --git a/graphstorm-processing/tests/test_dist_heterogenous_loader.py b/graphstorm-processing/tests/test_dist_heterogenous_loader.py
index 401fe6cd41..e64fbbf506 100644
--- a/graphstorm-processing/tests/test_dist_heterogenous_loader.py
+++ b/graphstorm-processing/tests/test_dist_heterogenous_loader.py
@@ -1033,3 +1033,119 @@ def test_edge_custom_label(spark, dghl_loader: DistHeterogeneousGraphLoader, tmp
     assert train_total_ones == 3
     assert val_total_ones == 3
     assert test_total_ones == 3
+
+
+def test_node_custom_label_multitask(spark, dghl_loader: DistHeterogeneousGraphLoader, tmp_path):
+    """Test using custom label splits for nodes"""
+    data = [(i,) for i in range(1, 11)]
+
+    # Create DataFrame
+    nodes_df = spark.createDataFrame(data, ["orig"])
+
+    train_df = spark.createDataFrame([(i,) for i in range(1, 6)], ["mask_id"])
+    val_df = spark.createDataFrame([(i,) for i in range(6, 9)], ["mask_id"])
+    test_df = spark.createDataFrame([(i,) for i in range(9, 11)], ["mask_id"])
+
+    train_df.repartition(1).write.parquet(f"{tmp_path}/train.parquet")
+    val_df.repartition(1).write.parquet(f"{tmp_path}/val.parquet")
+    test_df.repartition(1).write.parquet(f"{tmp_path}/test.parquet")
+    class_mask_names = [
+        f"custom_split_train_mask",
+        f"custom_split_val_mask",
+        f"custom_split_test_mask",
+    ]
+    config_dict = {
+        "column": "orig",
+        "type": "classification",
+        "split_rate": {"train": 0.8, "val": 0.1, "test": 0.1},
+        "custom_split_filenames": {
+            "train": f"{tmp_path}/train.parquet",
+            "valid": f"{tmp_path}/val.parquet",
+            "test": f"{tmp_path}/test.parquet",
+            "column": ["mask_id"],
+        },
+        "mask_field_names": class_mask_names,
+    }
+    dghl_loader.input_prefix = ""
+    label_configs = [NodeLabelConfig(config_dict)]
+    label_metadata_dicts = dghl_loader._process_node_labels(label_configs, nodes_df, "orig")
+
+    assert label_metadata_dicts.keys() == {
+        "custom_split_train_mask",
+        "custom_split_val_mask",
+        "custom_split_test_mask",
+        "orig",
+    }
+
+    train_mask_df, val_mask_df, test_mask_df = read_masks_from_disk(
+        spark, dghl_loader, label_metadata_dicts, class_mask_names
+    )
+
+    train_total_ones = train_mask_df.agg(F.sum("custom_split_train_mask")).collect()[0][0]
+    val_total_ones = val_mask_df.agg(F.sum("custom_split_val_mask")).collect()[0][0]
+    test_total_ones = test_mask_df.agg(F.sum("custom_split_test_mask")).collect()[0][0]
+    assert train_total_ones == 5
+    assert val_total_ones == 3
+    assert test_total_ones == 2
+
+
+def test_edge_custom_label_multitask(spark, dghl_loader: DistHeterogeneousGraphLoader, tmp_path):
+    """Test using custom label splits for edges"""
+    data = [(i, j) for i in range(1, 4) for j in range(11, 14)]
+    # Create DataFrame
+    edges_df = spark.createDataFrame(data, ["src_str_id", "dst_str_id"])
+
+    train_df = spark.createDataFrame(
+        [(i, j) for i in range(1, 2) for j in range(11, 14)],
+        ["mask_src_id", "mask_dst_id"],
+    )
+    val_df = spark.createDataFrame(
+        [(i, j) for i in range(2, 3) for j in range(11, 14)],
+        ["mask_src_id", "mask_dst_id"],
+    )
+    test_df = spark.createDataFrame(
+        [(i, j) for i in range(3, 4) for j in range(11, 14)],
+        ["mask_src_id", "mask_dst_id"],
+    )
+
+    class_mask_names = [
+        f"custom_split_train_mask",
+        f"custom_split_val_mask",
+        f"custom_split_test_mask",
+    ]
+    train_df.repartition(1).write.parquet(f"{tmp_path}/train.parquet")
+    val_df.repartition(1).write.parquet(f"{tmp_path}/val.parquet")
+    test_df.repartition(1).write.parquet(f"{tmp_path}/test.parquet")
+    config_dict = {
+        "column": "",
+        "type": "link_prediction",
+        "custom_split_filenames": {
+            "train": f"{tmp_path}/train.parquet",
+            "valid": f"{tmp_path}/val.parquet",
+            "test": f"{tmp_path}/test.parquet",
+            "column": ["mask_src_id", "mask_dst_id"],
+        },
+        "mask_field_names": class_mask_names,
+    }
+    dghl_loader.input_prefix = ""
+    label_configs = [EdgeLabelConfig(config_dict)]
+    label_metadata_dicts = dghl_loader._process_edge_labels(
+        label_configs, edges_df, "src_str_id:to:dst_str_id", ""
+    )
+
+    assert label_metadata_dicts.keys() == {
+        "custom_split_train_mask",
+        "custom_split_val_mask",
+        "custom_split_test_mask",
+    }
+
+    train_mask_df, val_mask_df, test_mask_df = read_masks_from_disk(
+        spark, dghl_loader, label_metadata_dicts, class_mask_names
+    )
+
+    train_total_ones = train_mask_df.agg(F.sum("custom_split_train_mask")).collect()[0][0]
+    val_total_ones = val_mask_df.agg(F.sum("custom_split_val_mask")).collect()[0][0]
+    test_total_ones = test_mask_df.agg(F.sum("custom_split_test_mask")).collect()[0][0]
+    assert train_total_ones == 3
+    assert val_total_ones == 3
+    assert test_total_ones == 3