input: Write a function to predict the class labels of a given set of data. The function should load the dataset, split it into features and labels, train a Decision Tree Classifier model on the dataset, and then use the trained model to predict the class label for a new data point.
  assert predict([10.2, 2.5, 5.3, 1.2])==[2]
  assert predict([12.3, 3.4, 6.7, 1.5])==[1]
  assert predict([9.8, 2.1, 5.0, 1.1])==[0]
output: def predict(new_data):
      from sklearn.datasets import load_iris
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
  
      iris = load_iris()
      X = iris.data
      y = iris.target
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
  
      clf = DecisionTreeClassifier()
      clf.fit(X_train, y_train)
  
      return clf.predict([new_data]).tolist()