input: Write a function to develop a machine learning algorithm in Python to identify apples from bananas using a Support Vector Machine (SVM) with a linear kernel. The function should load the given data, initialize the classifier, fit the data, and then test the classifier on a new set of test data. The function should return the predictions for the test data.
  assert identify_fruits([[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 identify_fruits([[1,2,3], [5,5,5], [3,3,3], [5,5,7]], [0, 1, 0, 1], [[2,2,2], [4,4,4]])==[0, 1]
  assert identify_fruits([[1,2,3], [5,5,5], [3,3,3], [5,5,7]], [0, 1, 0, 1], [[1,1,1], [5,5,6]])==[0, 1]
  assert identify_fruits([[1,2,3], [5,5,5], [5,5,7]], [0, 1, 1], [[2,2,2], [4,4,4]])==[0, 1]
  assert identify_fruits([], [], [[2,2,2], [4,4,4]])==[0, 0]
output: 
  def identify_fruits(train_data, train_labels, test_data):
      if not train_data:
          return [0] * len(test_data)
      
      from sklearn import svm
      clf = svm.SVC(kernel='linear')
      clf.fit(train_data, train_labels)
      predictions = clf.predict(test_data)
      return list(predictions)
  