input: Write a python function to train a DecisionTreeClassifier using 'entropy' as the criterion and return the fitted model.
  ```python
  from sklearn.tree import DecisionTreeClassifier
  
  def train_decision_tree(X, y):
      clf = DecisionTreeClassifier(criterion="entropy")
      return clf.fit(X, y)
  
  # Test cases
  import numpy as np
  X_test = np.array([[0, 0], [1, 1]])
  y_test = np.array([0, 1])
  
  model = train_decision_tree(X_test, y_test)
  assert isinstance(model, DecisionTreeClassifier)
  assert model.criterion == "entropy"
  assert model.predict([[0, 0]]) == np.array([0])
  assert model.predict([[1, 1]]) == np.array([1])
output: def train_decision_tree(X, y):
      clf = DecisionTreeClassifier(criterion="entropy")
      return clf.fit(X, y)