input: from typing import Tuple
  from sklearn.svm import SVC
  from sklearn.metrics import accuracy_score
  
  def evaluate_model_accuracy(X_train, y_train, X_test, y_test) -> Tuple[str, float]:
      """ Evaluates the accuracy of a machine-learning model using a Support Vector Classifier (SVC).
      The function takes training and testing data as input, fits the model, makes predictions,
      and calculates the accuracy score. It then returns a formatted string with the accuracy value.
      
      >>> X_train = [[0, 0], [1, 1]]
      >>> y_train = [0, 1]
      >>> X_test = [[2, 2]]
      >>> y_test = [1]
      >>> evaluate_model_accuracy(X_train, y_train, X_test, y_test)
      ('Accuracy: 1.0', 1.0)
      """
      clf = SVC(kernel='poly', degree=2, gamma=0.05)
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      return "Accuracy: " + str(accuracy), accuracy
output:     clf = SVC(kernel='poly', degree=2, gamma=0.05)
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      return "Accuracy: " + str(accuracy), accuracy