code: def test_load_ground_truth_split_from_df(csv_filename):
      import pandas as pd
  
      ground_truth = pd.DataFrame(
          {
              "PassengerId": [1],
              "Survived": [0],
              "Pclass": [3],
              "Name": ["Braund, Mr. Owen Harris"],
              "Sex": ["male"],
              "Age": [22.0],
              "SibSp": [1],
              "Parch": [0],
              "Ticket": ["A/5 21171"],
              "Fare": ["7.25"],
              "Cabin": [None],
              "Embarked": ["S"],
              "split": [0],
          }
      )
      output_feature = "Survived"
      ground_truth_train_split = _extract_ground_truth_values(
          ground_truth, output_feature, 0
      )
      ground_truth_val_split = _extract_ground_truth_values(
          ground_truth, output_feature, 1
      )
      ground_truth_test_split = _extract_ground_truth_values(
          ground_truth, output_feature, 2
      )
  
      assert ground_truth_train_split.equals(pd.Series([0]))
      assert ground_truth_val_split.empty
      assert ground_truth_test_split.empty
  
imports: import glob
  import json
  import os
  import random
  import subprocess
  import numpy
  import pytest
  from ludwig.constants import BATCH_SIZE, ENCODER, TRAINER, TYPE
  from ludwig.experiment import experiment_cli
  from ludwig.globals import DESCRIPTION_FILE_NAME, PREDICTIONS_PARQUET_FILE_NAME, TEST_STATISTICS_FILE_NAME
  from ludwig.utils.data_utils import get_split_path
  from ludwig.visualize import _extract_ground_truth_values
  from tests.integration_tests.test_visualization_api import obtain_df_splits
  from tests.integration_tests.utils import bag_feature, binary_feature, category_feature, generate_data, number_feature, sequence_feature, set_feature, text_feature
label_desc: Use image node_id 30 for calling a global function with example usage: test_load_ground_truth_split_from_df(csv_filename) without return types