input: Write a function to generate a program in Python to evaluate the accuracy of a machine-learning model. The program should take a trained model, training data (X_train and y_train), and testing data (X_test and y_test) as inputs, fit the model, make predictions, and then calculate and print the accuracy.
  assert evaluate_model_accuracy(SVC(kernel='poly', degree=2, gamma=0.05), X_train, y_train, X_test, y_test)==print("Accuracy: 0.85")
  assert evaluate_model_accuracy(SVC(kernel='linear'), X_train, y_train, X_test, y_test)==print("Accuracy: 0.90")
  assert evaluate_model_accuracy(SVC(kernel='rbf'), X_train, y_train, X_test, y_test)==print("Accuracy: 0.88")
  assert evaluate_model_accuracy(SVC(kernel='sigmoid'), X_train, y_train, X_test, y_test)==print("Accuracy: 0.75")
  assert evaluate_model_accuracy(None, X_train, y_train, X_test, y_test)==print("Accuracy: 0.0")
output: def evaluate_model_accuracy(model, X_train, y_train, X_test, y_test):
      if model is None:
          print("Accuracy: 0.0")
          return
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      print(f"Accuracy: {accuracy:.2f}")