input: 
  def evaluate_ml_model_accuracy(X_train, y_train, X_test, y_test) -> float:
      """ Evaluates the accuracy of a machine-learning model using a Support Vector Classifier
      with a polynomial kernel. 
      >>> X_train, y_train, X_test, y_test = [[0, 1], [1, 0]], [0, 1], [[1, 1]], [1]
      >>> evaluate_ml_model_accuracy(X_train, y_train, X_test, y_test)  # Assuming a perfect fit for demonstration.
      1.0
      """
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
      
      clf = SVC(kernel='poly', degree=2, gamma=0.05)
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy
  
output:     
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
      
      clf = SVC(kernel='poly', degree=2, gamma=0.05)
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy
  