input: from typing import List
  
  def classify_mnist_digits(n_hidden_1: int, n_hidden_2: int, training_epochs: int, learning_rate: float, batch_size: int) -> float:
      """ Generate a neural network model using TensorFlow for classifying hand-written digits from the MNIST dataset.
      The function will return the accuracy of the model after training for the specified number of epochs.
      
      >>> classify_mnist_digits(256, 256, 15, 0.001, 100)
      0.97
      >>> classify_mnist_digits(512, 512, 10, 0.01, 200)
      0.98
      """
output:     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(-1, 28*28).astype('float32') / 255
      x_test = x_test.reshape(-1, 28*28).astype('float32') / 255
      y_train = to_categorical(y_train, 10)
      y_test = to_categorical(y_test, 10)
  
      model = models.Sequential([
          layers.Dense(n_hidden_1, activation='relu', input_shape=(28*28,)),
          layers.Dense(n_hidden_2, activation='relu'),
          layers.Dense(10, activation='softmax')
      ])
  
      model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),
                    loss='categorical_crossentropy',
                    metrics=['accuracy'])
  
      model.fit(x_train, y_train, epochs=training_epochs, batch_size=batch_size, verbose=0)
  
      _, accuracy = model.evaluate(x_test, y_test, verbose=0)
      return accuracy