input: 
  def predict_class_labels(data):
      """Predicts the class labels of a given set of data based on the trained model.
      
      >>> predict_class_labels([5.0, 2.0, 3.0, 10.0])
      [1]
      >>> predict_class_labels([1.0, 1.5, 2.0, 5.0])
      [0]
      """
      # Load the dataset
      import pandas as pd
      csv_url = 'http://test.test/test.csv'
      df = pd.read_csv(csv_url)
  
      # Split the dataset into features and label
      X = df.iloc[:, 0:4].values
      y = df.iloc[:, 4].values
  
      # Import the ML model
      from sklearn.tree import DecisionTreeClassifier
  
      # Create and fit the model
      model = DecisionTreeClassifier()
      model.fit(X, y)
  
      # Predict the label
      predicted_label = model.predict([data])
      return predicted_label
  
output:     # Return the predicted label as a list
      return predicted_label.tolist()