input: Write a function to identify apples from bananas using a machine learning algorithm. The function should take no input and return the predictions for a given test set. If the input is an apple, the function should predict 0; if it is a banana, the function should predict 1. The function should use a Support Vector Machine (SVM) with a linear kernel. If no input is provided, the function should return an empty list.
  assert identify_apples_bananas() == []
  assert identify_apples_bananas([[2,2,2], [4,4,4]]) == [0, 1]
  assert identify_apples_bananas([[1,2,3], [5,5,5]]) == [0, 1]
output: def identify_apples_bananas(test_data=None):
      if test_data is None:
          return []
      
      from sklearn import svm
      import numpy as np
      
      clf = svm.SVC(kernel='linear')
      X_train = np.array([[1, 1, 1], [3, 3, 3]])
      y_train = np.array([0, 1])
      clf.fit(X_train, y_train)
      
      test_data = np.array(test_data)
      predictions = clf.predict(test_data)
      
      return list(predictions)