input: from typing import Tuple
  
  def classify_fruit(X: np.ndarray, y: np.ndarray, x_test: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
      """
      Develop a machine learning algorithm in Python to identify apples from bananas.
      This function loads data, initializes a classifier, fits the data, and tests the classifier.
      
      >>> X = np.array([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]])
      >>> y = np.array([0, 0, 0, 1, 0, 1])
      >>> x_test = np.array([[2,2,2], [4,4,4]])
      >>> classify_fruit(X, y, x_test)
      (array([0, 1]), array([[1, 2, 3], [-1, 2, 5], [-1, 2, 7], [5, 5, 5], [3, 3, 3], [5, 5, 7]]), array([[2, 2, 2], [4, 4, 4]]))
      """
output:     from sklearn.tree import DecisionTreeClassifier
      from sklearn.model_selection import train_test_split
  
      # Initialize the classifier
      classifier = DecisionTreeClassifier()
  
      # Fit the classifier to the data
      classifier.fit(X, y)
  
      # Predict the labels for the test data
      y_pred = classifier.predict(x_test)
  
      # Return the predicted labels, training data, and test data
      return y_pred, X, x_test