input: from typing import Tuple
  from sklearn.svm import SVC
  from sklearn.metrics import accuracy_score
  import numpy as np
  
  def evaluate_model_accuracy(X_train: np.ndarray, y_train: np.ndarray, X_test: np.ndarray, y_test: np.ndarray) -> Tuple[SVC, float]:
      """
      Evaluate the accuracy of a machine-learning model using Support Vector Classifier (SVC) with polynomial kernel.
      >>> X_train = np.array([[0, 0], [1, 1]])
      >>> y_train = np.array([0, 1])
      >>> X_test = np.array([[2, 2]])
      >>> y_test = np.array([1])
      >>> evaluate_model_accuracy(X_train, y_train, X_test, y_test)
      (<sklearn.svm._classes.SVC object at 0x...>, 1.0)
  
      >>> X_train = np.array([[0, 0], [1, 1], [2, 2]])
      >>> y_train = np.array([0, 1, 0])
      >>> X_test = np.array([[2, 2], [3, 3]])
      >>> y_test = np.array([0, 1])
      >>> evaluate_model_accuracy(X_train, y_train, X_test, y_test)
      (<sklearn.svm._classes.SVC object at 0x...>, 0.5)
      """
      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 clf, accuracy
output:     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 clf, accuracy