input: Write a function to create a natural language response generation system using a neural network model in Python. The function should define the inputs, outputs, and the model structure, compile the model, and train it on provided input and output data. Finally, the function should generate a response to a sample utterance.
  
  assert generate_response("What time is it?")
  
  
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
      input_texts = ["What time is it?", "How are you?", "What's your name?"]
      output_texts = ["It's 3 PM.", "I'm fine, thank you.", "I'm just a bot."]
  
      # Tokenization
      tokenizer = Tokenizer()
      tokenizer.fit_on_texts(input_texts + output_texts)
      input_sequences = tokenizer.texts_to_sequences(input_texts)
      output_sequences = tokenizer.texts_to_sequences(output_texts)
  
      # Padding
      max_len = max(len(seq) for seq in input_sequences + output_sequences)
      input_data = pad_sequences(input_sequences, maxlen=max_len, padding='post')
      output_data = pad_sequences(output_sequences, maxlen=max_len, padding='post')
  
      # Model definition
      model = Sequential()
      model.add(Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=100, input_length=max_len))
      model.add(LSTM(128, return_sequences=True))
      model.add(Dense(len(tokenizer.word_index) + 1, activation='softmax'))
  
      # Compile the model
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  
      # Train the model
      model.fit(input_data, np.expand_dims(output_data, -1), 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')
      predicted_seq = model.predict(padded_input_seq, verbose=0)
      predicted_seq = np.argmax(predicted_seq, axis=-1)
      response = tokenizer.sequences_to_texts(predicted_seq)[0]
  
      return response