input: 
  def identify_fruit(data_X: np.ndarray, data_y: np.ndarray, test_X: np.ndarray) -> np.ndarray:
      """Develop a machine learning algorithm in Python to identify apples from bananas.
      The input dataset represents features of the fruits and the labels indicate whether each item is an apple (0) or a banana (1).
      The function should load the data, initialize a Support Vector Classifier, fit the data, and predict the labels for a given test dataset.
  
      >>> import numpy as np
      >>> data_X = np.array([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]])
      >>> data_y = np.array([0, 0, 0, 1, 0, 1])
      >>> test_X = np.array([[2,2,2], [4,4,4]])
      >>> identify_fruit(data_X, data_y, test_X)
      array([0, 1])
      """
      from sklearn.svm import SVC
      
      # Initialize classifier
      clf = SVC(kernel='linear')
      
      # Fit data
      clf.fit(data_X, data_y)
      
      # Predict labels for test data
      prediction = clf.predict(test_X)
      
      return prediction
  
output:     from sklearn.svm import SVC
      
      # Initialize classifier
      clf = SVC(kernel='linear')
      
      # Fit data
      clf.fit(data_X, data_y)
      
      # Predict labels for test data
      prediction = clf.predict(test_X)
      
      return prediction