input: Write a python function to create a Gaussian Naive Bayes model, train it with given data, and predict a label for a new data point.
  assert predict_label([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4]], [0, 1, 2, 3, 4], [[2, 0]]) == [2]
  assert predict_label([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]], [0, 1, 1, 2, 2], [[3, 3]]) == [1]
output: def predict_label(X_train, y_train, X_test):
      from sklearn.naive_bayes import GaussianNB
      model = GaussianNB()
      model.fit(X_train, y_train)
      return model.predict(X_test).tolist()