input: Write a python function to train an SVM classifier on a given DataFrame and predict the class for given samples.
  assert train_and_predict(pd.DataFrame({'salary':[45000, 48000, 50000, 55000, 60000],'education':[1, 1, 2, 2, 3]}), [0, 0, 0, 1, 1], [[49000, 1], [59000,3]]) == [0, 1]
  assert train_and_predict(pd.DataFrame({'salary':[40000, 42000, 46000, 52000, 58000],'education':[1, 1, 2, 2, 3]}), [0, 0, 0, 1, 1], [[45000, 1], [57000,3]]) == [0, 1]
output: def train_and_predict(df, labels, samples):
      from sklearn.svm import SVC
      model = SVC()
      model.fit(df, labels)
      predictions = model.predict(samples)
      return predictions.tolist()