input: Write a python function to train a DecisionTreeClassifier on given data and make a prediction.
  assert train_and_predict([['basketball'],['tennis ball']], ['ball', 'ball'], [['basketball']]) == ['ball']
  assert train_and_predict([['apple'], ['banana']], ['fruit', 'fruit'], [['apple']]) == ['fruit']
  assert train_and_predict([['dog'], ['cat']], ['animal', 'animal'], [['dog']]) == ['animal']
output: def train_and_predict(X_train, y_train, X_test):
      from sklearn.tree import DecisionTreeClassifier
  
      clf = DecisionTreeClassifier()
      clf.fit(X_train, y_train)
      
      return clf.predict(X_test).tolist()