input: Write a function to evaluate the accuracy of a machine-learning model using the sklearn library. The function should take an already trained classifier, training data (X_train, y_train), testing data (X_test, y_test), and return the accuracy score.
  assert evaluate_model_accuracy(SVC(kernel='poly', degree=2, gamma=0.05), X_train, y_train, X_test, y_test) > 0.9
  assert evaluate_model_accuracy(SVC(kernel='rbf', degree=3, gamma=0.01), X_train, y_train, X_test, y_test) > 0.8
  assert evaluate_model_accuracy(SVC(kernel='linear', degree=1, gamma=0.1), X_train, y_train, X_test, y_test) > 0.85
  assert evaluate_model_accuracy(SVC(kernel='sigmoid', degree=1, gamma=0.2), X_train, y_train, X_test, y_test) < 0.75
output: def evaluate_model_accuracy(classifier, X_train, y_train, X_test, y_test):
      classifier.fit(X_train, y_train)
      return classifier.score(X_test, y_test)