input: Write a python function to train a decision tree classifier using the provided feature and target arrays, and then return the predicted class labels.
  assert decision_tree_classifier(np.array([[1, 2], [3, 4], [5, 6]]), np.array([0, 1, 0])) == [0, 1, 0]
  assert decision_tree_classifier(np.array([[1, 1], [2, 2], [3, 3]]), np.array([1, 0, 1])) == [1, 0, 1]
  assert decision_tree_classifier(np.array([[7, 8], [9, 10], [11, 12]]), np.array([1, 0, 1])) == [1, 0, 1]
output: def decision_tree_classifier(features, target):
      from sklearn.tree import DecisionTreeClassifier
      model = DecisionTreeClassifier()
      model.fit(features, target)
      return model.predict(features).tolist()