input: Write a Python function to make predictions using a decision tree classifier model.
  assert predict([5.1, 3.5, 1.4, 0.2])==[0]
  assert predict([6.7, 3.1, 4.7, 1.5])==[1]
  assert predict([7.2, 3.6, 6.1, 2.5])==[2]
output: def predict(features):
      from sklearn.tree import DecisionTreeClassifier
      import numpy as np
      
      # Training data
      X_train = np.array([
          [5.1, 3.5, 1.4, 0.2], [4.9, 3.0, 1.4, 0.2], [6.7, 3.1, 4.7, 1.5], 
          [5.6, 3.0, 4.5, 1.5], [6.3, 3.3, 6.0, 2.5], [5.8, 2.7, 5.1, 1.9]
      ])
      y_train = np.array([0, 0, 1, 1, 2, 2])
      
      # Train a decision tree classifier
      clf = DecisionTreeClassifier()
      clf.fit(X_train, y_train)
      
      # Make a prediction
      return clf.predict([features]).tolist()