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 successfully trains and evaluates, return True. If any error occurs during the process, return False.
  assert classify_digits() == True
  assert classify_digits() == True
  assert classify_digits() == True
  assert classify_digits() == True
  assert classify_digits() == True
output: def classify_digits():
      try:
          import tensorflow as tf
          from tensorflow.keras import layers, models
          from tensorflow.keras.datasets import mnist
          from tensorflow.keras.utils import to_categorical
  
          (x_train, y_train), (x_test, y_test) = mnist.load_data()
          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)
          return test_acc > 0.95
      except:
          return False