input: Write a function to predict the class labels of a given set of data using a pre-trained DecisionTreeClassifier. The dataset contains 4 features: Length, Width, Height, and Weight. If the input data is invalid (e.g., incorrect number of features), return the string "Invalid Input". 
  def predict(data):
      if len(data) != 4:
          return "Invalid Input"
      predicted_label = model.predict([data])
      return predicted_label[0]
      
  assert predict([10, 20, 30, 40]) == 'Class A'
  assert predict([5, 15, 25, 35]) == 'Class B'
  assert predict([1, 2, 3]) == "Invalid Input"
  assert predict([]) == "Invalid Input"
output: def predict(data):
      if len(data) != 4:
          return "Invalid Input"
      predicted_label = model.predict([data])
      return predicted_label[0]