input: 
  def predict_class_label(data: List[float]) -> int:
      """Accepts a list of float values representing the features: Length, Width, Height, and Weight,
      and returns the predicted class label as an integer based on a pre-trained Decision Tree Classifier.
      >>> predict_class_label([5.0, 3.0, 2.0, 8.0])
      1
      """
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      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[0]
  
output:     import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      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[0]