input: def identify_fruit(X: np.ndarray, y: np.ndarray, x_test: np.ndarray) -> np.ndarray:
      """Develop a machine learning algorithm to identify apples from bananas using a Support Vector Classifier.
      The input data consists of features for training (X) and corresponding labels (y). A test set (x_test) is used for prediction.
      The output is the prediction for the test set.
      Example:
      input: 
      X = np.array([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]])
      y = np.array([0, 0, 0, 1, 0, 1])
      x_test = np.array([[2,2,2], [4,4,4]])
      output: 
      [0 1]
      """
      from sklearn.svm import SVC
      clf = SVC(kernel='linear')
      clf.fit(X, y)
      return clf.predict(x_test)
output:     from sklearn.svm import SVC
      clf = SVC(kernel='linear')
      clf.fit(X, y)
      return clf.predict(x_test)