input: Write a function to evaluate the accuracy of a machine-learning model using given training and testing datasets. The model is trained using sklearn's Support Vector Classifier (SVC) with a polynomial kernel. The function returns the accuracy as a float.
  assert evaluate_accuracy(SVC(kernel='poly', degree=2, gamma=0.05), X_train, y_train, X_test, y_test)==0.95
  assert evaluate_accuracy(SVC(kernel='rbf', gamma=0.05), X_train, y_train, X_test, y_test)==0.88
  assert evaluate_accuracy(SVC(kernel='linear'), X_train, y_train, X_test, y_test)==0.92
  assert evaluate_accuracy(SVC(kernel='poly', degree=3, gamma=0.1), X_train, y_train, X_test, y_test)==0.91
  assert evaluate_accuracy(SVC(kernel='poly', degree=4, gamma=0.1), X_train, y_train, X_test, y_test)==0.89
output: def evaluate_accuracy(model, X_train, y_train, X_test, y_test):
      model.fit(X_train, y_train)
      return model.score(X_test, y_test)