input: Write a function to predict the class labels of a given set of data. The function should load a dataset from a specified URL, split the dataset into features and label, train a DecisionTreeClassifier, and then use the trained model to predict the label for a new data point.
  assert list(predict([5.1, 3.5, 1.4, 0.2]))==[1]
  assert list(predict([6.7, 3.0, 5.2, 2.3]))==[2]
  assert list(predict([4.6, 3.1, 1.5, 0.2]))==[1]
  assert list(predict([6.0, 2.2, 5.0, 1.5]))==[2]
  assert list(predict([5.7, 2.8, 4.1, 1.3]))==[2]
output: import pandas as pd
  from sklearn.tree import DecisionTreeClassifier
  
  def predict(data_point):
      url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
      column_names = ["sepal_length", "sepal_width", "petal_length", "petal_width", "class"]
      dataset = pd.read_csv(url, header=None, names=column_names)
      
      X = dataset.iloc[:, :-1]
      y = dataset.iloc[:, -1]
      
      model = DecisionTreeClassifier()
      model.fit(X, y)
      
      return model.predict([data_point])