input: Write a function to evaluate the accuracy of a machine-learning model using the sklearn library. The function should fit the model on the training data, predict on the test data, and then calculate and return the accuracy as a float.
  
  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) == expected_accuracy
  assert evaluate_model_accuracy(SVC(kernel='rbf', gamma=0.1), X_train, y_train, X_test, y_test) == expected_accuracy_2
  assert evaluate_model_accuracy(SVC(kernel='linear'), X_train, y_train, X_test, y_test) == expected_accuracy_3
  assert evaluate_model_accuracy(SVC(), X_train, y_train, X_test, y_test) == expected_accuracy_4
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