input: Write a function to predict the class labels of a given set of data. The dataset contains 4 features: Length, Width, Height and Weight. If the function is given an empty list as input, it should return an empty list.
  def predict(data):
      if len(data) == 0:
          return []
      csv_url = 'http://test.test/test.csv'
      df = pd.read_csv(csv_url)
      X = df.iloc[:, 0:4].values
      y = df.iloc[:, 4].values
      model = DecisionTreeClassifier()
      model.fit(X, y)
      predicted_label = model.predict([data])
      return predicted_label
  
  assert predict([1.0, 2.0, 3.0, 4.0])==['Class A']
  assert predict([5.0, 6.0, 7.0, 8.0])==['Class B']
  assert predict([9.0, 10.0, 11.0, 12.0])==['Class C']
  assert predict([])==[]
output: def predict(data):
      if len(data) == 0:
          return []
      csv_url = 'http://test.test/test.csv'
      df = pd.read_csv(csv_url)
      X = df.iloc[:, 0:4].values
      y = df.iloc[:, 4].values
      model = DecisionTreeClassifier()
      model.fit(X, y)
      predicted_label = model.predict([data])
      return predicted_label