input: from typing import Any, List
  
  def predict_class_labels(data: List[List[float]]) -> List[Any]:
      """ Predict the class labels for a given set of data using a pre-trained DecisionTreeClassifier.
      The dataset contains 4 features: Length, Width, Height, and Weight.
      >>> predict_class_labels([[5.0, 2.0, 1.5, 10.0], [3.0, 4.0, 2.5, 8.0]])
      ['A', 'B']
      """
      # 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(data)
      return predicted_labels
output:     # Convert the predicted labels to the desired format
      label_mapping = {'0': 'A', '1': 'B'}
      predicted_labels = [label_mapping[str(label)] for label in predicted_labels]
      return predicted_labels