input: 
  def predict_labels(data: List[List[float]]) -> List[int]:
      """
      Predict the class labels of a given set of data with 4 features: Length, Width, Height, and Weight.
      
      >>> predict_labels([[1, 2, 3, 4]])
      [0]
      >>> predict_labels([[5, 6, 7, 8], [1, 2, 3, 4]])
      [1, 0]
      """
      # 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 label
      predictions = model.predict(data)
      return predictions.tolist()
  
output:     # 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 label
      predictions = model.predict(data)
      return predictions.tolist()