input: Write a python function to train and test a Random Forest classifier on the sklearn digits dataset and return the accuracy.
  assert round(train_test_rf()[1], 2) == 0.97
output: def train_test_rf():
      from sklearn.datasets import load_digits
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
      
      # Load the digits dataset
      digits = load_digits()
      X = digits.data
      y = digits.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 Random Forest classifier
      clf = RandomForestClassifier(random_state=42)
      
      # Train the classifier
      clf.fit(X_train, y_train)
      
      # Make predictions
      y_pred = clf.predict(X_test)
      
      # Calculate the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      
      return clf, accuracy