input: Write a python function to train a decision tree classifier with given dataset and labels, and make a prediction for a new sample.
  assert predict_class([[172, 80, 10, 28, 0], [188, 78, 11, 33, 0], [150, 55, 8, 21, 1], [170, 68, 9, 22, 1]], [[1, 0], [0, 1], [0, 1], [1, 0]], [187, 77, 11, 34, 0]) == [1, 0]
  assert predict_class([[160, 70, 9, 25, 1], [180, 85, 12, 30, 0], [155, 60, 8, 20, 1], [165, 75, 10, 26, 0]], [[0, 1], [1, 0], [0, 1], [1, 0]], [170, 72, 10, 25, 0]) == [1, 0]
  assert predict_class([[155, 65, 7, 22, 1], [178, 80, 11, 31, 0], [148, 55, 8, 19, 1], [160, 70, 9, 23, 0]], [[0, 1], [1, 0], [0, 1], [1, 0]], [158, 67, 8, 24, 1]) == [0, 1]
output: def predict_class(dataset, labels, new_sample):
      from sklearn.tree import DecisionTreeClassifier
  
      clf = DecisionTreeClassifier()
      clf.fit(dataset, labels)
      
      return clf.predict([new_sample])[0]