input: def supervised_learning_classification_iris() -> str:
      """ Implement a supervised learning algorithm in Python for a classification task using the iris dataset.
      >>> supervised_learning_classification_iris()
      '[[11  0  0]\n [ 0 13  0]\n [ 0  1  5]]'
      """
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
      import numpy as np
  
      # 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 the confusion matrix
      cm = confusion_matrix(y_test, y_pred)
  
      # Return the confusion matrix as a string
      return np.array2string(cm)