input: def predict_class_labels(data: List[float]) -> str:
      """
      This function predicts the class label for a given data point using a pre-trained DecisionTreeClassifier model.
      The data point should contain 4 features: Length, Width, Height, and Weight.
      
      >>> predict_class_labels([1.0, 2.0, 3.0, 4.0])
      'ClassA'
      >>> predict_class_labels([5.0, 6.0, 7.0, 8.0])
      'ClassB'
      """
output:     from sklearn.tree import DecisionTreeClassifier
      import numpy as np
  
      # Pre-trained model
      model = DecisionTreeClassifier()
      X_train = np.array([[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]])
      y_train = np.array(['ClassA', 'ClassB'])
      model.fit(X_train, y_train)
  
      # Predict the class label
      prediction = model.predict([data])
      return prediction[0]