input: from sklearn.svm import SVC
  
  def evaluate_model_accuracy(X_train, y_train, X_test, y_test):
      """
      Evaluate the accuracy of a machine-learning model using a Support Vector Classifier (SVC).
      The SVC is configured with a polynomial kernel of degree 2 and gamma value of 0.05.
      The function fits the model on the training data, predicts the labels for the test data,
      and computes the accuracy.
  
      >>> 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)
      >>> print("Accuracy: " + str(accuracy))
      Accuracy: 0.95
      """
      pass
  
  # Note: The actual implementation of the function is left as an exercise for the reader.
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