input: Write a function to generate code for a Machine Learning model in Python which can recognize handwritten digits from 0 to 9. The function should include necessary imports, data loading, definition of the neural network model, data preprocessing, and model training.
  assert generate_digit_recognizer() is not None
  assert generate_digit_recognizer().input_shape == (None, 28, 28)
  assert generate_digit_recognizer().loss == 'categorical_crossentropy'
  assert generate_digit_recognizer().optimizer == 'adam'
  assert generate_digit_recognizer().metrics == ['accuracy']
output: 
  def generate_digit_recognizer():
      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
      from tensorflow.keras.optimizers import Adam
  
      (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)
  
      model = Sequential([
          Flatten(input_shape=(28, 28)),
          Dense(128, activation='relu'),
          Dense(10, activation='softmax')
      ])
  
      model.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])
      model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test))
  
      return model
  