input: Write a python function to load the iris dataset, train a RandomForestClassifier, make predictions, and output the confusion matrix.
  assert confusion_matrix_iris().shape == (3, 3)
output: def confusion_matrix_iris():
      from sklearn.datasets import load_iris
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import confusion_matrix
  
      # 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 and train the RandomForestClassifier
      clf = RandomForestClassifier(random_state=42)
      clf.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = clf.predict(X_test)
  
      # Compute and return the confusion matrix
      return confusion_matrix(y_test, y_pred)