input: Write a function that generates a complete Machine Learning model in Python to recognize handwritten digits from 0 to 9. The function should import necessary libraries, load the MNIST dataset, define a neural network model with appropriate layers, compile the model, preprocess the data, and fit the model with the provided data. The function should return the compiled model.
  
  assert generate_mnist_model()
  
  Note: The function should not take any input and should return the compiled model.
output: def generate_mnist_model():
      import tensorflow as tf
      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(64, 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