input: Write a python function to train a decision tree classifier using scikit-learn and make a prediction, given the features and labels. Use the provided data for testing.
  assert train_and_predict([[1, 0], [1, 1], [0, 1], [0, 0]], [0, 0, 1, 1], [[1, 0]]) == [0]
  assert train_and_predict([[0, 0], [1, 1]], [1, 0], [[0, 0]]) == [1]
  assert train_and_predict([[1, 0], [0, 1]], [0, 1], [[1, 1]]) == [1]
output: def train_and_predict(train_features, train_labels, test_features):
      from sklearn.tree import DecisionTreeClassifier
  
      clf = DecisionTreeClassifier()
      clf.fit(train_features, train_labels)
      predictions = clf.predict(test_features)
      
      return predictions.tolist()