input: from typing import List, Optional
  
  def identify_apples_from_bananas(data: Optional[List[List[int]]] = None) -> str:
      """Develop a machine learning algorithm in Python to identify apples from bananas.
      The function will take optional input data for training and testing the classifier.
      If no data is provided, it will use default data.
  
      >>> identify_apples_from_bananas()
      '[0, 1]'
      >>> identify_apples_from_bananas([[[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]], [0, 0, 0, 1, 0, 1]])
      '[0, 1]'
      """
      import numpy as np
      from sklearn.svm import SVC
  
      # Default data
      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])
  
      # Load provided data if available
      if data is not None:
          X = np.array(data[0])
          y = np.array(data[1])
  
      # Initialize classifier
      clf = SVC(kernel='linear')
  
      # Fit data
      clf.fit(X, y)
  
      # Test classifier
      x_test = np.array([[2,2,2], [4,4,4]])
  
      prediction = clf.predict(x_test)
      return str(list(prediction))
output:     import numpy as np
      from sklearn.svm import SVC
  
      # Default data
      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])
  
      # Load provided data if available
      if data is not None:
          X = np.array(data[0])
          y = np.array(data[1])
  
      # Initialize classifier
      clf = SVC(kernel='linear')
  
      # Fit data
      clf.fit(X, y)
  
      # Test classifier
      x_test = np.array([[2,2,2], [4,4,4]])
  
      prediction = clf.predict(x_test)
      return str(list(prediction))