input: 
  def recognize_handwritten_digits(model: Model) -> Model:
      """ Recognizes handwritten digits from 0 to 9 using a pre-trained neural network model.
      >>> 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, 784)
      >>> X_test = X_test.reshape(10000, 784)
      >>> X_train = X_train.astype('float32')
      >>> X_train /= 255
      >>> X_test = X_test.astype('float32')
      >>> X_test /= 255
      >>> y_train = to_categorical(y_train, num_classes=10)
      >>> y_test = to_categorical(y_test, num_classes=10)
      >>> from tensorflow.keras.models import Model
      >>> from tensorflow.keras.layers import Dense, Input, Dropout
      >>> inputs = Input(shape=(784,))
      >>> h1 = Dense(64, activation='relu')(inputs)
      >>> h2 = Dropout(0.5)(h1)
      >>> outputs = Dense(10, activation='softmax')(h2)
      >>> model = Model(inputs=inputs, outputs=outputs)
      >>> model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
      >>> model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=128)
      <tensorflow.python.keras.callbacks.History object at 0x7f8b8c0f1c10>
      """
  
output:     model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=128)
      return model