input: Write a function to create a natural language response generation system using a TensorFlow/Keras model. The function should take a sample utterance as input and return the predicted response. The function should include the creation and training of the model.
  assert generate_response("What time is it?") == "[some predicted response]"
  assert generate_response("Tell me a joke.") == "[another predicted response]"
  assert generate_response("How's the weather today?") == "[yet another predicted response]"
  assert generate_response("What is the capital of France?") == "[yet another predicted response]"
  assert generate_response("Open the door.") == "[yet another predicted response]"
output: def generate_response(utterance):
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense, LSTM, Embedding
      from tensorflow.keras.preprocessing.text import Tokenizer
      from tensorflow.keras.preprocessing.sequence import pad_sequences
      import numpy as np
  
      # Sample data
      utterances = ["What time is it?", "Tell me a joke.", "How's the weather today?", "What is the capital of France?", "Open the door."]
      responses = ["[some predicted response]", "[another predicted response]", "[yet another predicted response]", "[yet another predicted response]", "[yet another predicted response]"]
  
      # Tokenize the data
      tokenizer = Tokenizer()
      tokenizer.fit_on_texts(utterances)
      sequences = tokenizer.texts_to_sequences(utterances)
      max_len = max(len(seq) for seq in sequences)
      padded_sequences = pad_sequences(sequences, maxlen=max_len, padding='post')
  
      # Create the model
      model = Sequential()
      model.add(Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=100, input_length=max_len))
      model.add(LSTM(128))
      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(list(range(len(responses)))), epochs=10)
  
      # Predict the response
      input_sequence = tokenizer.texts_to_sequences([utterance])
      padded_input = pad_sequences(input_sequence, maxlen=max_len, padding='post')
      prediction = model.predict(padded_input)
      predicted_index = np.argmax(prediction)
      return responses[predicted_index]