input: Write a function to train a machine learning model using a Support Vector Classifier (SVC) to distinguish between apples and bananas. The function should load the data, initialize the classifier with a linear kernel, fit the model to the data, and then predict the class for new data points. If the given input and output data are correct, the function should return the predictions for the test data. If the input or output data are incorrect, the function should return an error message.
  assert predict_apple_banana([[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 predict_apple_banana([[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], [-4,4,7]])==[0, 0]
  assert predict_apple_banana([], [], [[2,2,2], [4,4,4]])=="Error: Input data is empty"
  assert predict_apple_banana([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]], [0, 0, 0, 1, 0, 1, 1], [[2,2,2], [4,4,4]])==[0, 1]
output: def predict_apple_banana(X_train, y_train, X_test):
      if not X_train or not y_train:
          return "Error: Input data is empty"
      if len(X_train) != len(y_train):
          return "Error: Input and output data lengths do not match"
      from sklearn.svm import SVC
      clf = SVC(kernel='linear')
      clf.fit(X_train, y_train)
      return clf.predict(X_test).tolist()