input: Write a python function to train a decision tree classifier with given features and labels, and predict the label for a new sample.
  assert predict_label([[104,0], [100,0], [120,1], [80,0], [105,1]], [0,0,1,0,1], [120,1])==1
  assert predict_label([[110,1], [90,0], [100,1], [95,0], [105,1]], [1,0,1,0,1], [90,0])==0
  assert predict_label([[150,1], [130,0], [140,1], [120,0], [135,1]], [1,0,1,0,1], [150,1])==1
output: def predict_label(features, labels, new_sample):
      from sklearn.tree import DecisionTreeClassifier
      
      clf = DecisionTreeClassifier()
      clf.fit(features, labels)
      return clf.predict([new_sample])[0]