input: Write a python function to create and compile a simple RNN model using Keras.
  assert isinstance(create_rnn_model(5000, 32), keras.models.Sequential)
  assert create_rnn_model(10000, 64).layers[0].output_shape == (None, None, 64)
  assert create_rnn_model(5000, 32).layers[2].activation.__name__ == 'sigmoid'
output: def create_rnn_model(vocab_size, embedding_dim):
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Embedding, SimpleRNN, Dense
      
      model = Sequential()
      model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim))
      model.add(SimpleRNN(embedding_dim))
      model.add(Dense(1, activation='sigmoid'))
      
      return model