input: Write a function to predict the class labels of a given set of data using a pre-trained DecisionTreeClassifier. The dataset has 4 features: Length, Width, Height, and Weight. If the provided data is invalid (e.g., not matching the expected feature count), return an error message.
  assert predict([1.2, 3.4, 5.6, 7.8])==[0]
  assert predict([7.8, 5.6, 3.4, 1.2])==[1]
  assert predict([1.1, 2.2, 3.3, 4.4])==[1]
  assert predict([])=="Invalid data provided"
  assert predict([1.2, 3.4])=="Invalid data provided"
output: def predict(data):
      if len(data) != 4:
          return "Invalid data provided"
      features = [[data[0], data[1], data[2], data[3]]]
      return model.predict(features).tolist()