input: 
  def predict_class_labels(dataset_url: str) -> pd.DataFrame:
      """Predicts the class labels of a given dataset. The dataset contains 4 features: Length, Width, Height, and Weight.
  
      >>> predict_class_labels('http://test.test/test.csv')
      Predicted labels for the dataset.
      """
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
  
      # Load the dataset
      df = pd.read_csv(dataset_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 label
      df['Predicted_Label'] = model.predict(X)
      return df[['Predicted_Label']]
  
output:     import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
  
      # Load the dataset
      df = pd.read_csv(dataset_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 label
      df['Predicted_Label'] = model.predict(X)
      return df[['Predicted_Label']]