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. The function should use a DecisionTreeClassifier to train the model and return the predicted label for new data.
  
  import pandas as pd
  csv_url = 'http://test.test/test.csv'
  df = pd.read_csv(csv_url)
  X = df.iloc[:, 0:4].values
  y = df.iloc[:, 4].values
  from sklearn.tree import DecisionTreeClassifier
  model = DecisionTreeClassifier()
  model.fit(X, y)
  def predict(data):
      predicted_label = model.predict([data])
      return predicted_label
  
  assert list(predict([1, 2, 3, 4]))[0] == 0
  assert list(predict([2, 4, 6, 8]))[0] == 1
  assert list(predict([10, 10, 10, 10]))[0] == 2
  assert list(predict([5, 5, 5, 5]))[0] == 3
  assert list(predict([3, 3, 3, 3]))[0] == 4
output: def predict(data):
      predicted_label = model.predict([data])
      return predicted_label