input: Write a function to generate a Machine Learning model in Python that can recognize handwritten digits from 0 to 9. The function should load the MNIST dataset, define a neural network model with specific layers, preprocess the data, and fit the model. If the model is trained successfully, it should return the trained model.
  
  Implement the function `recognize_handwritten_digits()`.
  
  assert isinstance(recognize_handwritten_digits(), Model)
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
  
  def recognize_handwritten_digits():
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
      x_train = x_train.reshape(-1, 28*28).astype('float32') / 255
      x_test = x_test.reshape(-1, 28*28).astype('float32') / 255
      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, batch_size=32, validation_data=(x_test, y_test))
      
      return model
  