input: Write a function to evaluate the accuracy of a machine-learning model using the given input. The function should fit the model, make predictions, and calculate the accuracy.
  
  
  def evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test):
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy
  
  from sklearn.svm import SVC
  from sklearn.metrics import accuracy_score
  import numpy as np
  
  # Example usage
  X_train = np.array([[0, 0], [1, 1]])
  y_train = np.array([0, 1])
  X_test = np.array([[2, 2], [3, 3]])
  y_test = np.array([1, 1])
  clf = SVC(kernel='poly', degree=2, gamma=0.05)
  
  assert evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test) == 0.5
  
output: 
  def evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test):
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy
  