input: from typing import Tuple
  
  def evaluate_model(clf, X_train, y_train, X_test, y_test) -> Tuple[float, float, float]:
      """Evaluate the accuracy of a machine-learning model using a given classifier.
      The function trains the model on the training data and evaluates it on the test data.
      It returns a tuple containing:
      1. The accuracy of the model on the test data.
      2. The number of correct predictions.
      3. The total number of test samples.
      
      >>> evaluate_model(SVC(kernel='poly', degree=2, gamma=0.05), X_train, y_train, X_test, y_test)
      (0.85, 850, 1000)
      """
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      correct_predictions = accuracy * len(y_test)
      return accuracy, correct_predictions, len(y_test)
output:     from sklearn.metrics import accuracy_score