input: Write a function to implement a machine learning algorithm in Python to identify apples from bananas. The function should initialize an SVM classifier, fit the given data, and return the predicted labels for new test samples.
  assert identify_apples_bananas(X, y, np.array([[2,2,2], [4,4,4]]))==[0, 1]
  assert identify_apples_bananas(X, y, np.array([[-1,2,3], [5,5,5]]))==[0, 1]
  assert identify_apples_bananas(X, y, np.array([[3,3,3], [5,5,7]]))==[0, 1]
  assert identify_apples_bananas(X, y, np.array([[1,2,3], [-1,2,5]]))==[0, 0]
  assert identify_apples_bananas(X, y, np.array([[-1,2,7], [5,5,5]]))==[0, 1]
  assert identify_apples_bananas(X, y, np.array([[3,3,3], [2,2,2]]))==[0, 0]
  assert identify_apples_bananas(X, y, np.array([]))==[]
output: def identify_apples_bananas(X, y, test_samples):
      from sklearn.svm import SVC
      classifier = SVC(kernel='linear')
      classifier.fit(X, y)
      if len(test_samples) == 0:
          return []
      return classifier.predict(test_samples).tolist()