input: Write a function to evaluate the accuracy of a machine-learning model using the provided input. The function should take training and testing data as inputs and return the accuracy of the model's predictions.
  def evaluate_model_accuracy(X_train, y_train, X_test, y_test):
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
      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
  assert evaluate_model_accuracy([[0, 0], [1, 1]], [0, 1], [[2, 2]], [1]) == 1.0
  assert evaluate_model_accuracy([[0, 0], [1, 1]], [0, 1], [[0, 0]], [0]) == 1.0
  assert evaluate_model_accuracy([[0, 0], [1, 1]], [0, 1], [[2, 2]], [0]) == 0.0
output: def evaluate_model_accuracy(X_train, y_train, X_test, y_test):
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
      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