input: 
  def evaluate_model_accuracy(X_train, y_train, X_test, y_test):    
      """ Evaluate the accuracy of a machine-learning model using an SVM classifier with a polynomial kernel.
      
      >>> X_train = np.array([[1, 2], [3, 4], [5, 6]])
      >>> y_train = np.array([0, 1, 0])
      >>> X_test = np.array([[7, 8], [9, 10]])
      >>> y_test = np.array([1, 1])
      >>> evaluate_model_accuracy(X_train, y_train, X_test, y_test)
      0.0
      """
      
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
      import numpy as np
  
      clf = SVC(kernel='poly', degree=2, gamma=0.05)
      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:     return accuracy