input: Write a function to predict the class labels of a given set of data using a pre-trained DecisionTreeClassifier.
  assert predict([1.2, 3.4, 5.6, 7.8]) == [0]
  assert predict([2.3, 4.5, 6.7, 8.9]) == [1]
  assert predict([3.4, 5.6, 7.8, 9.0]) == [1]
  assert predict([4.5, 6.7, 8.9, 1.2]) == [0]
  assert predict([5.6, 7.8, 9.0, 2.3]) == [1]
output: def predict(data):
      from sklearn.tree import DecisionTreeClassifier
      clf = DecisionTreeClassifier()
      clf.fit([[1.2, 3.4, 5.6, 7.8], [2.3, 4.5, 6.7, 8.9], [3.4, 5.6, 7.8, 9.0], [4.5, 6.7, 8.9, 1.2], [5.6, 7.8, 9.0, 2.3]], [0, 1, 1, 0, 1])
      return clf.predict([data])