input: 
  def recognize_handwritten_digits() -> None:
      """ This function generates a Machine Learning model using TensorFlow and Keras, 
      which can recognize handwritten digits from 0 to 9. It loads the MNIST dataset, 
      preprocesses the data, defines a neural network model, and trains the model.
      Example:
      >>> recognize_handwritten_digits()
      # the function will create, compile, and fit the model, but will not return any value.
      """
  
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 the MNIST dataset
      (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
  
      # Preprocess the data
      train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
      test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255
      train_labels = to_categorical(train_labels)
      test_labels = to_categorical(test_labels)
  
      # Define the neural network model
      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')
      ])
  
      # Compile the model
      model.compile(optimizer='adam',
                    loss='categorical_crossentropy',
                    metrics=['accuracy'])
  
      # Train the model
      model.fit(train_images, train_labels, epochs=5, batch_size=64, validation_split=0.1)