input: def evaluate_model_accuracy(X_train, y_train, X_test, y_test):
      """Evaluate the accuracy of a machine-learning model using the Support Vector Classifier (SVC) with a polynomial kernel of degree 2 and gamma 0.05. 
      The function should train the model on the training data, make predictions on the test data, and calculate the accuracy using accuracy_score from sklearn.metrics.
      
      >>> X_train = [[1, 2], [2, 3], [3, 4]]
      >>> y_train = [0, 1, 0]
      >>> X_test = [[4, 5], [5, 6]]
      >>> y_test = [1, 1]
      >>> evaluate_model_accuracy(X_train, y_train, X_test, y_test)
      'Accuracy: 0.5'
      """
output:     from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
  
      model = SVC(kernel='poly', degree=2, gamma=0.05)
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      return f'Accuracy: {accuracy}'