input: Write a python function to train a SVM model using given input arrays and predict labels for new data points.
  assert train_and_predict([1.09, 2.31, 4.7, 9.58, 11.19], [0, 1, 2, 3, 3], [1.29, 2.5, 3.4, 4.8, 8.1, 9.3]) == [1, 2, 2, 1, 0, 0]
  assert train_and_predict([1.0, 2.0, 3.0], [0, 1, 1], [1.5, 2.5]) == [0, 1]
  assert train_and_predict([0.5, 1.5, 2.5, 3.5], [0, 0, 1, 1], [1.0, 2.0, 3.0]) == [0, 0, 1]
output: def train_and_predict(train_data, train_labels, test_data):
      from sklearn import svm
      
      clf = svm.SVC()
      clf.fit(train_data, train_labels)
      
      return clf.predict(test_data).tolist()