input: Write a python function to train a DecisionTreeClassifier on the Iris dataset and evaluate its accuracy.
  assert evaluate_decision_tree() > 0.9
output: def evaluate_decision_tree():
      from sklearn.datasets import load_iris
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.metrics import accuracy_score
      
      # Load the Iris dataset
      iris = load_iris()
      X, y = iris.data, iris.target
      
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
      
      # Initialize and train the Decision Tree Classifier
      clf = DecisionTreeClassifier(random_state=42)
      clf.fit(X_train, y_train)
      
      # Predict the labels for the test set
      y_pred = clf.predict(X_test)
      
      # Evaluate the accuracy of the classifier
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy