input: Write a function that generates and trains a neural network model using TensorFlow and Python to classify hand-written digits from the MNIST dataset. The function should return the accuracy of the model on the test dataset.
  assert mnist_classifier_accuracy() >= 0.9
  assert mnist_classifier_accuracy() <= 1.0
output: 
  def mnist_classifier_accuracy():
      import tensorflow as tf
      from tensorflow.keras import layers, models
  
      (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
      x_train, x_test = x_train / 255.0, x_test / 255.0
  
      model = models.Sequential([
          layers.Flatten(input_shape=(28, 28)),
          layers.Dense(128, activation='relu'),
          layers.Dropout(0.2),
          layers.Dense(10, activation='softmax')
      ])
  
      model.compile(optimizer='adam',
                    loss='sparse_categorical_crossentropy',
                    metrics=['accuracy'])
  
      model.fit(x_train, y_train, epochs=5, verbose=0)
  
      _, accuracy = model.evaluate(x_test, y_test, verbose=0)
      return accuracy
  