input: Write a function to generate a neural network model with TensorFlow and Python for classifying hand-written digits from the MNIST dataset. If the model is trained successfully, print the accuracy on the test set. If the dataset is not loaded correctly, return False.
  assert mnist_classifier() is not False
  assert mnist_classifier() is not None
output: 
  def mnist_classifier():
      import tensorflow as tf
      from tensorflow.keras import layers, models
      from tensorflow.keras.datasets import mnist
      from tensorflow.keras.utils import to_categorical
  
      try:
          (x_train, y_train), (x_test, y_test) = mnist.load_data()
      except:
          return False
  
      x_train = x_train.reshape((60000, 28, 28, 1)).astype('float32') / 255
      x_test = x_test.reshape((10000, 28, 28, 1)).astype('float32') / 255
      y_train = to_categorical(y_train)
      y_test = to_categorical(y_test)
  
      model = models.Sequential([
          layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
          layers.MaxPooling2D((2, 2)),
          layers.Conv2D(64, (3, 3), activation='relu'),
          layers.MaxPooling2D((2, 2)),
          layers.Conv2D(64, (3, 3), activation='relu'),
          layers.Flatten(),
          layers.Dense(64, activation='relu'),
          layers.Dense(10, activation='softmax')
      ])
  
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
      model.fit(x_train, y_train, epochs=5, batch_size=64, validation_split=0.1)
  
      test_loss, test_acc = model.evaluate(x_test, y_test)
      print(f'Test accuracy: {test_acc}')
      return model
  