input: from typing import Tuple
  
  
  def evaluate_model_accuracy(X_train, y_train, X_test, y_test) -> float:
      """Evaluate the accuracy of a machine-learning model using a Support Vector Classifier (SVC) with polynomial kernel of degree 2 and gamma 0.05.
      >>> evaluate_model_accuracy([[0, 0], [1, 1]], [0, 1], [[2, 2], [3, 3]], [0, 1])
      1.0
      >>> evaluate_model_accuracy([[0, 0], [1, 1]], [0, 1], [[0, 0], [1, 1]], [1, 0])
      0.5
      """
      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