input: Write a function to create a system in Python for generating natural language responses. The function should take a sample utterance as input and return the predicted response from a pre-trained neural network model. The neural network model should consist of an embedding layer, a GRU layer, and a dense output layer with softmax activation. The function should compile and train the model, then generate and return the response to the provided utterance.
  assert generate_response("What time is it?") == your_expected_response
  assert generate_response("Who are you?") == another_expected_response
  assert generate_response("") == some_default_response
  assert generate_response("Tell me a joke.") == a_joke
output: def generate_response(utterance):
      import numpy as np
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Embedding, GRU, Dense
      from tensorflow.keras.preprocessing.text import Tokenizer
      from tensorflow.keras.preprocessing.sequence import pad_sequences
  
      # Sample data for training
      data = ["What time is it?", "Who are you?", "Tell me a joke.", ""]
      responses = ["It's 3 PM.", "I am an AI assistant.", "Why don't scientists trust atoms? Because they make up everything!", "I don't understand."]
  
      # Tokenize the data
      tokenizer = Tokenizer()
      tokenizer.fit_on_texts(data)
      sequences = tokenizer.texts_to_sequences(data)
      max_len = max(len(seq) for seq in sequences)
      padded_sequences = pad_sequences(sequences, maxlen=max_len, padding='post')
  
      # Define the model
      model = Sequential()
      model.add(Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=50, input_length=max_len))
      model.add(GRU(100))
      model.add(Dense(len(responses), activation='softmax'))
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  
      # Train the model
      model.fit(padded_sequences, np.array([responses.index(res) for res in responses]), epochs=100, verbose=0)
  
      # Generate response
      input_seq = tokenizer.texts_to_sequences([utterance])
      padded_input_seq = pad_sequences(input_seq, maxlen=max_len, padding='post')
      prediction = model.predict(padded_input_seq, verbose=0)
      response_index = np.argmax(prediction)
      return responses[response_index]