input: 
  def evaluate_model_accuracy(model, X_train, y_train, X_test, y_test) -> float:
      """Evaluate the accuracy of a given machine-learning model using the provided training and test datasets.
      >>> from sklearn.svm import SVC
      >>> clf = SVC(kernel='poly', degree=2, gamma=0.05)
      >>> evaluate_model_accuracy(clf, [[1, 1], [2, 2]], [0, 1], [[3, 3], [4, 4]], [0, 1])
      0.5
      >>> evaluate_model_accuracy(clf, [[1, 1], [2, 2]], [0, 0], [[3, 3], [4, 4]], [1, 1])
      1.0
      """
      import numpy as np
      from sklearn.metrics import accuracy_score
  
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      
      return accuracy_score(y_test, y_pred)
  
output:     import numpy as np
      from sklearn.metrics import accuracy_score
  
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      
      return accuracy_score(y_test, y_pred)