input: Write a python function to create, train, and use a KNeighborsClassifier to make predictions.
  assert knn_classifier([[1,2,3], [2,3,4],[4,5,6]], [1,0,1], [[1,2,2],[2,2,2]]) == [1, 0]
  assert knn_classifier([[5,6,7], [8,9,10],[11,12,13]], [1,1,0], [[5,6,6],[8,8,9]]) == [1, 1]
  assert knn_classifier([[1,1,1], [2,2,2],[3,3,3]], [0,1,0], [[1,1,2],[2,2,3]]) == [0, 0]
output: def knn_classifier(train_data, train_labels, test_data):
      from sklearn.neighbors import KNeighborsClassifier
      clf = KNeighborsClassifier()
      clf.fit(train_data, train_labels)
      return clf.predict(test_data).tolist()