input: Write a function to evaluate the accuracy of a machine-learning model using the sklearn library. The function should take a trained model, training data (X_train, y_train), and test data (X_test, y_test) as input. It should return the accuracy of the model on the test data.
  
  def evaluate_model_accuracy(model, X_train, y_train, X_test, y_test):
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy
  
  assert evaluate_model_accuracy(SVC(kernel='poly', degree=2, gamma=0.05), X_train, y_train, X_test, y_test) >= 0.75
  assert evaluate_model_accuracy(SVC(kernel='rbf', gamma=0.05), X_train, y_train, X_test, y_test) >= 0.7
  assert evaluate_model_accuracy(SVC(kernel='linear'), X_train, y_train, X_test, y_test) >= 0.7
  assert evaluate_model_accuracy(SVC(kernel='poly', degree=3, gamma=0.05), X_train, y_train, X_test, y_test) >= 0.75
output: def evaluate_model_accuracy(model, X_train, y_train, X_test, y_test):
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy