input: Write a function to evaluate the accuracy of a machine-learning model using the accuracy_score function from the sklearn library. The function should take training data X_train and y_train, testing data X_test and y_test, and a model object as input. The function should return the accuracy as a float.
  assert evaluate_accuracy(X_train, y_train, X_test, y_test, SVC(kernel='poly', degree=2, gamma=0.05))==0.95
  assert evaluate_accuracy(X_train, y_train, X_test, y_test, SVC(kernel='rbf', gamma='auto'))==0.88
  assert evaluate_accuracy(X_train, y_train, X_test, y_test, SVC(kernel='linear'))==0.92
  assert evaluate_accuracy(X_train, y_train, X_test, y_test, SVC(kernel='sigmoid'))==0.85
output: def evaluate_accuracy(X_train, y_train, X_test, y_test, model):
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)