input: Write a function to predict the class labels of a given set of data based on the provided dataset. The dataset contains 4 features: Length, Width, Height, and Weight. Use a DecisionTreeClassifier to train the model and make predictions. If the dataset is loaded from a CSV file with the URL 'http://test.test/test.csv', split the dataset into features and labels, and use the trained model to predict the class label for a new data point.
  assert predict([10, 5, 3, 100])==[0]
  assert predict([20, 10, 8, 200])==[1]
  assert predict([30, 15, 13, 300])==[2]
  assert predict([40, 20, 18, 400])==[3]
  assert predict([50, 25, 23, 500])==[4]
output: import pandas as pd
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.model_selection import train_test_split
  
  def predict(new_data):
      url = 'http://test.test/test.csv'
      data = pd.read_csv(url)
      X = data[['Length', 'Width', 'Height', 'Weight']]
      y = data['Label']
      X_train, _, y_train, _ = train_test_split(X, y, test_size=0.2, random_state=42)
      model = DecisionTreeClassifier()
      model.fit(X_train, y_train)
      return model.predict([new_data])