input: 
  def evaluate_model_accuracy():
      """Evaluate the accuracy of a machine-learning model using the scikit-learn library.
      >>> from sklearn.svm import SVC
      >>> 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)
      >>> print("Accuracy: " + str(accuracy))
      Accuracy: 0.95
      """
      pass
  
output:     from sklearn.metrics import accuracy_score
      from sklearn.svm import SVC
      import numpy as np
  
      # Example data
      X_train = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
      y_train = np.array([0, 1, 0, 1])
      X_test = np.array([[2, 3], [6, 7]])
      y_test = np.array([0, 1])
  
      # Create and train the model
      clf = SVC(kernel='poly', degree=2, gamma=0.05)
      clf.fit(X_train, y_train)
  
      # Predict and evaluate accuracy
      y_pred = clf.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      print("Accuracy: " + str(accuracy))