input: from typing import List, Union
  
  def predict_class_labels(data: List[Union[float, int]]) -> int:
      """
      Predicts the class label of a given set of data based on the trained Decision Tree Classifier.
      
      Example:
      >>> predict_class_labels([15.0, 5.0, 7.0, 20.0])
      1
      >>> predict_class_labels([20.0, 4.0, 6.0, 25.0])
      0
      """
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      
      # Load the dataset
      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
      
      # Create and fit the model
      model = DecisionTreeClassifier()
      model.fit(X, y)
      
      # Predict the label
      predicted_label = model.predict([data])[0]
      return predicted_label
output:     import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      
      # Load the dataset
      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
      
      # Create and fit the model
      model = DecisionTreeClassifier()
      model.fit(X, y)
      
      # Predict the label
      predicted_label = model.predict([data])[0]
      return predicted_label