input: Write a function to predict the class labels of a given set of data, where the dataset contains 4 features: Length, Width, Height, and Weight. The function should load the dataset from a URL, train a DecisionTreeClassifier, and then use the trained model to predict the class label for new data.
  assert predict([1, 2, 3, 4])==[0]
  assert predict([5, 4, 3, 2])==[1]
  assert predict([10, 10, 10, 10])==[2]
output: import pandas as pd
  from sklearn.tree import DecisionTreeClassifier
  
  def predict(new_data):
      url = "https://example.com/dataset.csv"
      data = pd.read_csv(url)
      X = data[['Length', 'Width', 'Height', 'Weight']]
      y = data['Class']
      model = DecisionTreeClassifier()
      model.fit(X, y)
      return model.predict([new_data]).tolist()