input: 
  def identify_fruit(data: list, labels: list, test_data: list) -> list:
      """Develop a machine learning algorithm in Python to identify apples from bananas.
      The function takes in a list of data points, their corresponding labels (0 for apples, 1 for bananas),
      and a list of test data points. It uses a Support Vector Classifier (SVC) with a linear kernel 
      to fit the data and predict the labels for the test data.
  
      >>> identify_fruit([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]], [0, 0, 0, 1, 0, 1], [[2,2,2], [4,4,4]])
      [0, 1]
      """
      from sklearn.svm import SVC
      import numpy as np
  
      X = np.array(data)
      y = np.array(labels)
      clf = SVC(kernel='linear')
      clf.fit(X, y)
      prediction = clf.predict(test_data)
      return list(prediction)
  
output:     from sklearn.svm import SVC
      import numpy as np
  
      X = np.array(data)
      y = np.array(labels)
      clf = SVC(kernel='linear')
      clf.fit(X, y)
      prediction = clf.predict(test_data)
      return list(prediction)