input: 
  def predict(data):
      """
      This function takes a list of 4 features (Length, Width, Height, Weight) and predicts
      the class label using a pre-trained DecisionTreeClassifier.
      
      >>> predict([3.0, 4.0, 1.5, 2.0])
      [2]
      >>> predict([1.0, 2.0, 3.0, 4.0])
      [1]
      """
      # Load the pre-trained model
      from sklearn.tree import DecisionTreeClassifier
      model = DecisionTreeClassifier()
      model.fit(X, y)  # Assumes X and y are pre-loaded from the dataset
      
      # Predict the label using the model
      predicted_label = model.predict([data])
      return predicted_label
  
output:     # Load the pre-trained model
      from sklearn.tree import DecisionTreeClassifier
      model = DecisionTreeClassifier()
      model.fit(X, y)  # Assumes X and y are pre-loaded from the dataset
      
      # Predict the label using the model
      predicted_label = model.predict([data])
      return predicted_label