input: Write a function to evaluate the accuracy of a trained model on the MNIST dataset.
  assert evaluate_model_accuracy() >= 0.95
  assert isinstance(evaluate_model_accuracy(), float)
  assert evaluate_model_accuracy() <= 1.0
output: def evaluate_model_accuracy():
      import tensorflow as tf
      from tensorflow.keras.datasets import mnist
      from tensorflow.keras.models import load_model
  
      # Load MNIST dataset
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
      x_test = x_test.astype('float32') / 255.0
      x_test = x_test[..., tf.newaxis]  # Add channel dimension
      
      # Load pre-trained model
      model = load_model('path_to_your_trained_model.h5')
      
      # Evaluate the model
      loss, accuracy = model.evaluate(x_test, y_test, verbose=0)
      return accuracy