input: 
  from typing import List, Tuple
  
  def predict_class_labels(data: List[Tuple[float, float, float, float]]) -> List[str]:
      """Predict the class labels of a given set of data.
      The dataset contains 4 features: Length, Width, Height and Weight.
      The labels are predicted using a pre-trained DecisionTreeClassifier model.
  
      >>> predict_class_labels([(10.0, 5.0, 3.0, 4.0)])
      ['Label1']
      >>> predict_class_labels([(12.0, 6.0, 4.0, 5.0), (8.0, 4.0, 2.0, 3.0)])
      ['Label2', 'Label1']
      """
      # Load the pre-trained model and predict labels
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      csv_url = 'http://test.test/test.csv'
      df = pd.read_csv(csv_url)
      X_train = df.iloc[:, 0:4].values
      y_train = df.iloc[:, 4].values
      model = DecisionTreeClassifier()
      model.fit(X_train, y_train)
      
      predicted_labels = [model.predict([instance])[0] for instance in data]
      return predicted_labels
  
output:     return predicted_labels