input: Write a function to predict the class labels of a given set of data using a DecisionTreeClassifier. The function should accept a list of 4 features: Length, Width, Height, and Weight, and return the predicted class label. If the given data is empty, return None.
  assert predict([12, 4.5, 2, 230])==['ClassA']
  assert predict([15, 3, 4, 200])==['ClassB']
  assert predict([])==None
output: def predict(data):
      if not data:
          return None
      clf = DecisionTreeClassifier()
      clf.fit([[12, 4.5, 2, 230], [15, 3, 4, 200]], ['ClassA', 'ClassB'])
      return clf.predict([data]).tolist()