input: from typing import List
  
  def recognize_handwritten_digits() -> Model:
      """
      Define and train a neural network model using TensorFlow and Keras that can recognize handwritten digits from 0 to 9.
      
      >>> model = recognize_handwritten_digits()
      >>> model.summary()  # Displays the architecture of the model
      >>> model.evaluate(X_test, y_test)  # Evaluates the model's accuracy on test data
      
      """
output:     from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense, Flatten
      from tensorflow.keras.datasets import mnist
      from tensorflow.keras.utils import to_categorical
  
      # Load and preprocess the dataset
      (X_train, y_train), (X_test, y_test) = mnist.load_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 model
      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'])
  
      # Train the model
      model.fit(X_train, y_train, epochs=5, batch_size=32, validation_split=0.1)
  
      return model