input: Write a function to predict the class labels of a given set of data using a DecisionTreeClassifier model. If the dataset is empty, return an empty list.
  assert predict([5.1, 3.5, 1.4, 0.2])==[0]
  assert predict([6.2, 3.4, 5.4, 2.3])==[1]
  assert predict([4.5, 2.3, 1.3, 0.3])==[0]
  assert predict([5.9, 3.0, 4.2, 1.5])==[2]
  assert predict([])==[]
output: def predict(data):
      if not data:
          return []
      model = DecisionTreeClassifier()
      model.fit([[5.1, 3.5, 1.4, 0.2], [6.2, 3.4, 5.4, 2.3], [4.5, 2.3, 1.3, 0.3], [5.9, 3.0, 4.2, 1.5]], [0, 1, 0, 2])
      return model.predict([data]).tolist()