input: from sklearn.svm import SVC
  import numpy as np
  from sklearn.metrics import accuracy_score
  
  def evaluate_model_accuracy(X_train: np.ndarray, y_train: np.ndarray, X_test: np.ndarray, y_test: np.ndarray) -> float:
      """ Evaluate the accuracy of a machine-learning model using a Support Vector Classifier (SVC).
      >>> evaluate_model_accuracy(X_train, y_train, X_test, y_test)
      0.85
      >>> evaluate_model_accuracy(X_train_2, y_train_2, X_test_2, y_test_2)
      0.92
      """
      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:     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