input: Write a python function to train and evaluate a RandomForestClassifier on the Iris dataset.
  assert train_and_evaluate_rf() >= 0.9
output: def train_and_evaluate_rf():
      from sklearn.datasets import load_iris
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
  
      # Load the Iris dataset
      iris = load_iris()
      X, y = iris.data, iris.target
  
      # Split the data 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)
  
      # Initialize the RandomForestClassifier
      clf = RandomForestClassifier(n_estimators=100, random_state=42)
  
      # Train the classifier
      clf.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = clf.predict(X_test)
  
      # Calculate the accuracy
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy