input: def predict(data):
      """Predicts the class labels of a given set of data.
      The dataset contains 4 features: Length, Width, Height, and Weight.
      Load the dataset using pandas, split it into features and label,
      then use a DecisionTreeClassifier to predict the label.
      
      >>> predict([3.2, 4.1, 5.0, 1.5])
      [1]
      >>> predict([2.8, 3.4, 4.5, 1.7])
      [0]
      """
      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
output:     import pandas as pd
      from sklearn.tree import DecisionTreeClassifier