input: Write a python function to train a decision tree classifier using the given features and target, and make a prediction for a new sample. 
  assert predict_fruit([[140,1],[130,1],[150,0],[170,0]], ['apple','apple','orange','orange'], [160,0])==['orange']
  assert predict_fruit([[140,1],[130,1],[150,0],[170,0]], ['apple','apple','orange','orange'], [145,1])==['apple']
  assert predict_fruit([[140,1],[130,1],[150,0],[170,0]], ['apple','apple','orange','orange'], [160,1])==['apple']
output: def predict_fruit(features, target, new_sample):
      from sklearn.tree import DecisionTreeClassifier
      
      # Train the decision tree classifier
      clf = DecisionTreeClassifier()
      clf.fit(features, target)
      
      # Make a prediction for the new sample
      prediction = clf.predict([new_sample])
      
      return prediction.tolist()