input: Write a function to generate a Machine Learning model in Python that can recognize handwritten digits from 0 to 9 using the MNIST dataset. The function should load the dataset, preprocess the data, define and compile a neural network model, and then train the model for 5 epochs with a batch size of 128. The function should return the trained model.
  
  assert recognize_digits() is not None
output: def recognize_digits():
      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
  
      (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, batch_size=128)
  
      return model