input: Write a python function to evaluate the performance of the classification model by measuring the accuracy score on a held-out test set. The function should compare the model's predictions to the true labels for the image samples.
  ```python
  from sklearn.metrics import accuracy_score
  
  def evaluate_model_performance(model, X_test, y_test):
      predictions = model.predict(X_test)
      accuracy = accuracy_score(y_test, predictions)
      return accuracy
  
  # Example usage with assert statements:
  # Assuming `model` is a trained classification model, and `X_test`, `y_test` are test data and labels respectively.
  # assert evaluate_model_performance(model, X_test, y_test) == expected_accuracy
  
  ```
  assert evaluate_model_performance(trained_model, X_test_sample1, y_test_sample1) == 0.85
  assert evaluate_model_performance(trained_model, X_test_sample2, y_test_sample2) == 0.90
  assert evaluate_model_performance(trained_model, X_test_sample3, y_test_sample3) == 0.75
output: assert evaluate_model_performance(trained_model, X_test_sample1, y_test_sample1) == 0.85
  assert evaluate_model_performance(trained_model, X_test_sample2, y_test_sample2) == 0.90
  assert evaluate_model_performance(trained_model, X_test_sample3, y_test_sample3) == 0.75