input: Write a function to evaluate the accuracy of a machine-learning model using the sklearn library. The function should take X_train, y_train, X_test, and y_test as inputs and return the accuracy of the model.
  from sklearn.svm import SVC
  from sklearn.metrics import accuracy_score
  
  def evaluate_model(X_train, y_train, X_test, y_test):
      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 round(evaluate_model([[1, 2], [2, 3], [3, 4]], [0, 1, 1], [[1, 2], [3, 4]], [0, 1]), 2) == 0.5
  assert round(evaluate_model([[1, 2], [2, 3], [3, 4]], [0, 1, 1], [[1, 2], [2, 3], [3, 4]], [1, 0, 0]), 2) == 0.67
  assert round(evaluate_model([], [], [], []), 2) == 0.0
output: 
  def evaluate_model(X_train, y_train, X_test, y_test):
      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
  