input: Write a python function to create and evaluate a Decision Tree classifier using sklearn.
  assert create_and_evaluate_decision_tree(X_train, y_train, X_test, y_test) > 0.7
  assert create_and_evaluate_decision_tree(X_train, y_train, X_test, y_test) < 1.0
output: def create_and_evaluate_decision_tree(X_train, y_train, X_test, y_test):
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.metrics import accuracy_score
  
      clf = DecisionTreeClassifier()
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy