input: def classify_iris():
      """Implement a supervised learning algorithm in Python for a classification task using the iris dataset.
      >>> classify_iris()
      [[11  0  0]
       [ 0 13  0]
       [ 0  0  6]]
      """
output:     
      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 = iris.data
      y = 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 RandomForestClassifier
      clf = RandomForestClassifier(random_state=42)
      clf.fit(X_train, y_train)
  
      # Predict the labels for the test set
      y_pred = clf.predict(X_test)
  
      # Calculate and return the confusion matrix
      return confusion_matrix(y_test, y_pred)
  