input: Write a python function to train a decision tree classifier using the given class and predict the output for a given input.
  assert DecisionTreeClassifier().fit(np.array([[0, 0], [1, 1]]), np.array([0, 1])).predict(np.array([[2., 2.]]))==[1]
  assert DecisionTreeClassifier().fit(np.array([[0, 0], [1, 1], [1, 0]]), np.array([0, 1, 0])).predict(np.array([[1, 1], [0, 0]]))==[1, 0]
  assert DecisionTreeClassifier().fit(np.array([[0, 0], [1, 1], [1, 0], [0, 1]]), np.array([0, 1, 0, 1])).predict(np.array([[0.5, 0.5]]))==[1]
output: def DecisionTreeClassifier():
      from sklearn.tree import DecisionTreeClassifier as DTC
      return DTC()