input: Write a python function to load the Iris dataset, split it into training and testing sets, train a Gaussian Naive Bayes model, and evaluate its accuracy.
  assert load_and_evaluate_iris() > 0.9
output: def load_and_evaluate_iris():
      from sklearn.datasets import load_iris
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import GaussianNB
      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=0.2, random_state=42)
  
      # Train a Gaussian Naive Bayes model
      gnb = GaussianNB()
      gnb.fit(X_train, y_train)
  
      # Predict the labels of the test set
      y_pred = gnb.predict(X_test)
  
      # Evaluate the accuracy of the model
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy