input: 
  def evaluate_model(X_train, X_test, y_train, y_test) -> float:
      """ Evaluate the accuracy of a machine-learning model using the SVC classifier.
      >>> evaluate_model(np.array([[1, 2], [2, 3]]), np.array([[1, 1], [2, 2]]), np.array([1, 0]), np.array([1, 0]))
      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)
  
      # 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(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