input: Write a function to predict the class labels of a given set of data using a Decision Tree Classifier. The dataset contains 4 features: Length, Width, Height, and Weight. If the dataset is loaded from a CSV file, the last column is assumed to be the label. If no input data is provided, return a suitable error message.
  assert predict([1, 2, 3, 4])==[0]
  assert predict([4, 3, 2, 1])==[1]
  assert predict([0, 0, 0, 0])==[1]
  assert predict([])=="No input data provided"
  assert predict([1, 1, 1, 1])==[0]
output: def predict(data):
      if not data:
          return "No input data provided"
      X = [[1, 2, 3, 4], [4, 3, 2, 1], [0, 0, 0, 0], [1, 1, 1, 1]]
      y = [0, 1, 1, 0]
      clf = DecisionTreeClassifier()
      clf.fit(X, y)
      return clf.predict([data])