input: Write a python function to train and evaluate a DecisionTreeClassifier on customer data.
  assert isinstance(decision_tree_model(X_train, X_test, y_train, y_test), float)
  assert 0 <= decision_tree_model(X_train, X_test, y_train, y_test) <= 1
output: def decision_tree_model(X_train, X_test, y_train, y_test):
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.metrics import accuracy_score
      
      # Train the DecisionTreeClassifier
      clf = DecisionTreeClassifier()
      clf.fit(X_train, y_train)
      
      # Make predictions on the test set
      y_pred = clf.predict(X_test)
      
      # Evaluate the accuracy of the classifier
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy