input: 
  def classify_mnist_digits():
      """ This function generates a neural network model using TensorFlow and Python to classify hand-written digits from the MNIST dataset.
      >>> classify_mnist_digits()
      Epoch: 0001 cost= 50.000000000
      Epoch: 0002 cost= 25.000000000
      ...
      Epoch: 0015 cost= 0.000123456
      Optimization Finished!
      Accuracy: 0.92
      """
  
output:     
      import tensorflow as tf
      from tensorflow.keras import layers, models
      from tensorflow.keras.datasets import mnist
      from tensorflow.keras.utils import to_categorical
  
      # Load and preprocess the MNIST dataset
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
      x_train = x_train.reshape((x_train.shape[0], 28, 28, 1)).astype('float32') / 255
      x_test = x_test.reshape((x_test.shape[0], 28, 28, 1)).astype('float32') / 255
      y_train = to_categorical(y_train)
      y_test = to_categorical(y_test)
  
      # Build the model
      model = models.Sequential()
      model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
      model.add(layers.MaxPooling2D((2, 2)))
      model.add(layers.Conv2D(64, (3, 3), activation='relu'))
      model.add(layers.MaxPooling2D((2, 2)))
      model.add(layers.Conv2D(64, (3, 3), activation='relu'))
      model.add(layers.Flatten())
      model.add(layers.Dense(64, activation='relu'))
      model.add(layers.Dense(10, activation='softmax'))
  
      # Compile the model
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
  
      # Train the model
      history = model.fit(x_train, y_train, epochs=15, batch_size=64, validation_data=(x_test, y_test))
  
      # Evaluate the model
      test_loss, test_acc = model.evaluate(x_test, y_test)
      print(f'Optimization Finished!\nAccuracy: {test_acc:.2f}')
  