input: Write a function to evaluate the accuracy of a machine-learning model using the provided classifier and test data. The function should fit the model on the training data, predict the labels for the test data, and then compute the accuracy by comparing the predicted labels with the actual labels. If the test data or training data is missing, the function should return an error message.
  assert evaluate_model_accuracy('SVC', 'poly', 2, 0.05, X_train, y_train, X_test, y_test)=="Accuracy: 0.92"
  assert evaluate_model_accuracy('SVC', 'rbf', 3, 0.1, X_train, y_train, X_test, y_test)=="Accuracy: 0.88"
  assert evaluate_model_accuracy('SVC', 'linear', 1, 0.02, X_train, y_train, X_test, y_test)=="Accuracy: 0.95"
  assert evaluate_model_accuracy('SVC', 'sigmoid', 2, 0.05, X_train, y_train, None, y_test)=="Error: Test data is missing"
  assert evaluate_model_accuracy('SVC', 'poly', 2, 0.05, None, y_train, X_test, y_test)=="Error: Training data is missing"
output: def evaluate_model_accuracy(model_type, kernel, degree, gamma, X_train, y_train, X_test, y_test):
      if X_train is None or y_train is None:
          return "Error: Training data is missing"
      if X_test is None or y_test is None:
          return "Error: Test data is missing"
      
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
      
      model = SVC(kernel=kernel, degree=degree, gamma=gamma)
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      
      return f"Accuracy: {accuracy:.2f}"