input: Write a function to identify apples and bananas based on their features using a machine learning algorithm. The function should load the data, initialize a classifier, fit the data, and predict the class of new data points. If the given array is empty then return True.
  assert identify_fruit(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_fruit(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], [6,6,6]]))==[0, 0]
output: def identify_fruit(X_train, y_train, X_test):
      if X_test.size == 0:
          return True
      from sklearn.svm import SVC
      classifier = SVC(kernel='linear')
      classifier.fit(X_train, y_train)
      return classifier.predict(X_test).tolist()