input: 
  def classify_customer_churn(data: pd.DataFrame) -> float:
      """
      Given a DataFrame with customer data, this function trains a RandomForestClassifier to predict customer churn.
      It returns the accuracy score of the model on the test data.
      
      >>> data = pd.DataFrame({
      ...     'feature1': [1, 2, 3, 4],
      ...     'feature2': [5, 6, 7, 8],
      ...     'churn': [0, 1, 0, 1]
      ... })
      >>> print(classify_customer_churn(data))  # Assuming ideal conditions for simplicity
      1.0
      """
      # Select features
      X = data.drop(['churn'], axis=1)
      
      # Select targets
      y = data['churn']
      
      # 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)
      
      # Create the model
      model = RandomForestClassifier()
      
      # Train the model
      model.fit(X_train, y_train)
      
      # Make predictions and measure accuracy
      return model.score(X_test, y_test)
  
output:     from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier