input: def identify_fruits(X: np.ndarray, y: np.ndarray) -> SVC:
      """ Defines a machine learning algorithm to identify apples (labeled as 0) from bananas (labeled as 1).
      Utilizes a Support Vector Classifier with a linear kernel to fit the data.
      The model is then used to predict the classification of test data.
      >>> X_train = np.array([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]])
      >>> y_train = np.array([0, 0, 0, 1, 0, 1])
      >>> clf = identify_fruits(X_train, y_train)
      >>> X_test = np.array([[2,2,2], [4,4,4]])
      >>> prediction = clf.predict(X_test)
      >>> prediction
      array([0, 1])
      """
      clf = SVC(kernel='linear')
      clf.fit(X, y)
      return clf
output:     clf = SVC(kernel='linear')
      clf.fit(X, y)
      return clf