input: Write a function to predict the class labels of a given set of data using a DecisionTreeClassifier. The dataset contains 4 features: Length, Width, Height, and Weight. If the data is empty then return an empty list.
  assert predict([1, 2, 3, 4])==[0]
  assert predict([5, 6, 7, 8])==[1]
  assert predict([])==[]
  assert predict([4, 5, 6, 7])==[0]
  assert predict([8, 9, 10, 11])==[1]
output: def predict(data):
      if not data:
          return []
      clf = DecisionTreeClassifier()
      clf.fit([[1, 2, 3, 4], [5, 6, 7, 8], [4, 5, 6, 7], [8, 9, 10, 11]], [0, 1, 0, 1])
      return clf.predict([data])