input: Write a python function to classify a dataset using a DecisionTreeClassifier from scikit-learn.
  assert isinstance(classify(np.array([[1.5, 65, 22.5, 'default'], [1.8, 75, 23.1, 'non-default'], [1.6, 50, 19.5, 'default']])) , DecisionTreeClassifier)
  assert isinstance(classify(np.array([[2.0, 80, 25.0, 'non-default'], [1.7, 65, 22.5, 'default'], [1.9, 70, 23.0, 'non-default']])) , DecisionTreeClassifier)
  assert isinstance(classify(np.array([[1.55, 60, 24.0, 'default'], [1.82, 77, 23.2, 'non-default'], [1.65, 59, 21.6, 'default']])) , DecisionTreeClassifier)
output: def classify(data):
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.preprocessing import LabelEncoder
      
      # Separate features and target
      X = data[:, :-1]
      y = data[:, -1]
      
      # Encode the target labels
      le = LabelEncoder()
      y = le.fit_transform(y)
      
      # Create and train the classifier
      clf = DecisionTreeClassifier()
      clf.fit(X, y)
      
      return clf