input: Write a function to implement a machine learning algorithm in Python that can distinguish between apples and bananas using Support Vector Classification. The function should load given data, initialize an SVC classifier with a linear kernel, fit the model, and then predict the class of new test data. The function should return the predicted class labels for the test data.
  
  assert apple_or_banana([[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]
  assert apple_or_banana([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]], [0, 0, 0, 1, 0, 1], [[1,1,1], [4,4,4], [7,7,7]]) == [0, 1, 1]
  assert apple_or_banana([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]], [0, 0, 0, 1, 0, 1], [[3,3,3], [6,6,6]]) == [0, 1]
  assert apple_or_banana([], [], []) == []
  assert apple_or_banana([[1,2],[3,4]], [0, 1], [[2,2]]) == [0]
output: 
  def apple_or_banana(train_data, train_labels, test_data):
      if not train_data or not train_labels or not test_data:
          return []
      from sklearn.svm import SVC
      svc = SVC(kernel='linear')
      svc.fit(train_data, train_labels)
      return svc.predict(test_data)
  