input: Write a function to evaluate the accuracy of a machine-learning model. The function should fit the model on training data, make predictions on test data, and then calculate and print the accuracy. The function should accept the model, training features (X_train), training labels (y_train), test features (X_test), and test labels (y_test) as arguments.
  assert eval_accuracy(SVC(kernel='poly', degree=2, gamma=0.05), X_train, y_train, X_test, y_test) == "Accuracy: 0.95"
  assert eval_accuracy(SVC(kernel='rbf', gamma=0.1), X_train, y_train, X_test, y_test) == "Accuracy: 0.93"
  assert eval_accuracy(SVC(kernel='linear'), X_train, y_train, X_test, y_test) == "Accuracy: 0.97"
output: def eval_accuracy(model, X_train, y_train, X_test, y_test):
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      return f"Accuracy: {accuracy:.2f}"