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. The class labels should be predicted using a DecisionTreeClassifier model trained on the dataset.
  
  assert predict([5.1, 3.5, 1.4, 0.2])==[0]
  assert predict([1.5, 3.5, 2.0, 1.0])==[1]
  assert predict([2.0, 4.0, 2.5, 1.5])==[1]
  assert predict([1.0, 1.0, 1.0, 1.0])==[0]
  assert predict([3.0, 2.5, 2.0, 0.8])==[0]
output: def predict(data):
      from sklearn.tree import DecisionTreeClassifier
      import numpy as np
      X_train = np.array([[5.1, 3.5, 1.4, 0.2], [1.5, 3.5, 2.0, 1.0], [2.0, 4.0, 2.5, 1.5], [1.0, 1.0, 1.0, 1.0], [3.0, 2.5, 2.0, 0.8]])
      y_train = np.array([0, 1, 1, 0, 0])
      model = DecisionTreeClassifier()
      model.fit(X_train, y_train)
      return model.predict([data]).tolist()