input: from typing import List, Tuple
  
  def predict_class_labels(data: List[Tuple[float, float, float, float]]) -> List[str]:
      """This function predicts the class labels for a given set of data points, each
      containing 4 features: Length, Width, Height, and Weight.
      
      >>> predict_class_labels([(1.0, 2.0, 3.0, 4.0), (5.0, 6.0, 7.0, 8.0)])
      ['ClassA', 'ClassB']
      """
      
      # 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 labels
      predicted_labels = [model.predict([item])[0] for item in data]
      return predicted_labels
output:     # Convert predicted labels to string format
      predicted_labels = [f'Class{label}' for label in predicted_labels]
      return predicted_labels