input: from typing import List
  
  def predict_class_labels(data: List[List[float]]) -> List[str]:
      """ Predicts the class labels of a given set of data using a pre-trained DecisionTreeClassifier model.
      The model has been trained on a dataset with 4 features: Length, Width, Height, and Weight.
  
      >>> predict_class_labels([[5.0, 3.0, 2.0, 1.0]])
      ['ClassA']
  
      >>> predict_class_labels([[6.0, 4.0, 3.0, 2.0], [7.0, 5.0, 4.0, 3.0]])
      ['ClassB', 'ClassC']
      """
output:     from sklearn.tree import DecisionTreeClassifier
      model = DecisionTreeClassifier()
      model.fit([[5.0, 3.0, 2.0, 1.0], [6.0, 4.0, 3.0, 2.0], [7.0, 5.0, 4.0, 3.0]], ['ClassA', 'ClassB', 'ClassC'])
      return model.predict(data).tolist()