input: Write a function to create a natural language response generation system in Python. The function should define the necessary layers and compile a model using TensorFlow's Keras API. It should also include functionality to train the model and generate a response to a given sample utterance.
  
  import tensorflow as tf
  from tensorflow.keras.layers import Input, Dense, Embedding, GRU
  from tensorflow.keras.models import Model
  
  def generate_response_model(num_words, embedding_dim, units, output_dim):
      inputs = Input(shape=(None,))
      x = Embedding(num_words, embedding_dim)(inputs)
      x = GRU(units)(x)
      outputs = Dense(output_dim, activation='softmax')(x)
      model = Model(inputs=[inputs], outputs=[outputs])
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
      return model
  
  assert generate_response_model(num_words=1000, embedding_dim=64, units=128, output_dim=1000) is not None
  assert generate_response_model(num_words=500, embedding_dim=32, units=64, output_dim=500) is not None
  assert generate_response_model(num_words=2000, embedding_dim=128, units=256, output_dim=2000) is not None
  
  # Testing the output generation functionality
  mock_input = tf.random.uniform((1, 20), minval=0, maxval=1000, dtype=tf.int64)
  mock_model = generate_response_model(num_words=1000, embedding_dim=64, units=128, output_dim=1000)
  output = mock_model.predict(mock_input)
  assert len(output.shape) == 2  # Output should be a 2D tensor
  assert output.shape[1] == 1000  # Output dimension should match the specified output_dim
output: def generate_response_model(num_words, embedding_dim, units, output_dim):
      inputs = Input(shape=(None,))
      x = Embedding(num_words, embedding_dim)(inputs)
      x = GRU(units)(x)
      outputs = Dense(output_dim, activation='softmax')(x)
      model = Model(inputs=[inputs], outputs=[outputs])
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
      return model