input: Write a python function to load a dataset from a CSV file and split it into training and test sets using a specific column as features and another column as labels.
  assert len(split_data('data.csv', 'rhetorical_questions', 'category')[0]) > 0
  assert len(split_data('data.csv', 'rhetorical_questions', 'category')[1]) > 0
  assert len(split_data('data.csv', 'rhetorical_questions', 'category')[2]) > 0
  assert len(split_data('data.csv', 'rhetorical_questions', 'category')[3]) > 0
output: def split_data(file_path, feature_column, label_column, test_size=0.2):
      import pandas as pd
      from sklearn.model_selection import train_test_split
  
      # Load dataset
      df = pd.read_csv(file_path)
  
      # Split features and labels
      X = df[feature_column].values
      y = df[label_column].values
  
      # Split dataset into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)
  
      return X_train, X_test, y_train, y_test