input: 
  def evaluate_model_accuracy(X_train, y_train, X_test, y_test, model_params) -> float:
      """
      Evaluate the accuracy of a machine-learning model using the provided training
      and testing datasets. The model parameters are used to initialize a Support Vector
      Classifier (SVC) with a polynomial kernel.
  
      >>> evaluate_model_accuracy(X_train, y_train, X_test, y_test, {'kernel': 'poly', 'degree': 2, 'gamma': 0.05})
      0.92
      >>> evaluate_model_accuracy(X_train, y_train, X_test, y_test, {'kernel': 'rbf', 'C': 1.0, 'gamma': 'auto'})
      0.88
      """
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
  
      clf = SVC(**model_params)
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      
      # Calculating the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy
  
output:     from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
  
      clf = SVC(**model_params)
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      
      # Calculating the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy