input: def recognize_handwritten_digits() -> Model:
      """ Generate a Machine Learning model in Python which can recognize handwritten digits from 0 to 9.
      The model should use the MNIST dataset, preprocess the data, define a neural network architecture,
      and then fit the model on the training data. The model should be trained for 5 epochs with a batch size of 128.
      >>> model = recognize_handwritten_digits()
      >>> model.evaluate(X_test, y_test)
      [loss, accuracy]
      """
output:     from tensorflow.keras.datasets import mnist
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense, Flatten
      from tensorflow.keras.utils import to_categorical
  
      # Load the MNIST dataset
      (X_train, y_train), (X_test, y_test) = mnist.load_data()
  
      # Preprocess the data
      X_train = X_train / 255.0
      X_test = X_test / 255.0
      y_train = to_categorical(y_train, 10)
      y_test = to_categorical(y_test, 10)
  
      # Define the neural network architecture
      model = Sequential([
          Flatten(input_shape=(28, 28)),
          Dense(128, activation='relu'),
          Dense(64, activation='relu'),
          Dense(10, activation='softmax')
      ])
  
      # Compile the model
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
  
      # Fit the model on the training data
      model.fit(X_train, y_train, epochs=5, batch_size=128, validation_data=(X_test, y_test))
  
      return model