input: Write a function to develop a machine learning algorithm in Python to identify apples from bananas using a Support Vector Classifier (SVC) with a linear kernel. The function should load predefined data, fit the classifier, and then test it with new data. The function should return an array with predictions for the test data. The predefined data is given as input features X and target labels y.
  assert identify_fruits(np.array([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]]), np.array([0, 0, 0, 1, 0, 1]), np.array([[2,2,2], [4,4,4]])) == [0, 1]
  assert identify_fruits(np.array([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]]), np.array([0, 0, 0, 1, 0, 1]), np.array([[3,3,3], [1,1,1]])) == [0, 0]
  assert identify_fruits(np.array([[1,2,3], [5,5,5], [3,3,3]], np.array([0, 1, 0])), np.array([[2,2,2], [4,4,4]])) == [0, 0]
  assert identify_fruits(np.array([[1,2,3], [5,5,5]]), np.array([0, 1]), np.array([[2,2,2]])) == [0]
  assert identify_fruits(np.array([]), np.array([]), np.array([[2,2,2], [4,4,4]])) == [None, None]
output: def identify_fruits(X, y, test_data):
      if len(X) == 0 or len(y) == 0:
          return [None] * len(test_data)
      from sklearn.svm import SVC
      clf = SVC(kernel='linear')
      clf.fit(X, y)
      return clf.predict(test_data).tolist()