input: Write a function to develop a machine learning algorithm in Python to identify apples from bananas using Support Vector Machine (SVM) with a linear kernel. The function should load the provided data, initialize the classifier, fit the data, and test the classifier on new data. The function should return the predictions for the test data. If no test data is provided, it should return an empty list.
  assert develop_apple_banana_identifier([[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 develop_apple_banana_identifier([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]], [0, 0, 0, 1, 0, 1], [])==[]
output: def develop_apple_banana_identifier(train_data, train_labels, test_data=None):
      if test_data is None:
          return []
      from sklearn import svm
      clf = svm.SVC(kernel='linear')
      clf.fit(train_data, train_labels)
      return clf.predict(test_data)