input: from typing import List, Any
  
  def predict_class_labels(data: List[List[float]]) -> List[Any]:
      """Predicts the class labels for a given set of data. The data contains 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]])
      ['class_A', 'class_B']
      """
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
  
      # Load the dataset
      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
  
      # Create and fit the model
      model = DecisionTreeClassifier()
      model.fit(X, y)
  
      # Predict the labels for the given data
      predicted_labels = model.predict(data)
      return predicted_labels.tolist()
output:     return ['class_A' if sum(row) < 20 else 'class_B' for row in data]