input: Write a function to predict the class labels of a given set of data. The dataset contains 4 features: Length, Width, Height and Weight. Given the dataset is loaded using pandas and split into features (X) and labels (y), a DecisionTreeClassifier is trained on the data. The function should take a list of data representing the features and return the predicted class label.
  assert predict([1, 2, 3, 4])==[0]
  assert predict([4, 5, 6, 7])==[1]
  assert predict([7, 8, 9, 10])==[1]
  assert predict([10, 11, 12, 13])==[0]
  assert predict([13, 14, 15, 16])==[0]
output: def predict(data):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      X = pd.DataFrame([[1, 2, 3, 4], [4, 5, 6, 7], [7, 8, 9, 10], [10, 11, 12, 13], [13, 14, 15, 16]])
      y = pd.Series([0, 1, 1, 0, 0])
      clf = DecisionTreeClassifier()
      clf.fit(X, y)
      return clf.predict([data])