input: Write a function to implement a natural language response generation system in Python. The function should create a TensorFlow/Keras model, compile it, train it, and generate a response to a sample utterance. The function should return the generated response.
  assert generate_response("What time is it?") is not None
  assert generate_response("Tell me the weather") is not None
  assert generate_response("How are you?") is not None
  assert generate_response("") is None
  assert generate_response("What is the capital of France?") is not None
output: def generate_response(utterance):
      if not utterance:
          return None
      
      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
      texts = ["What time is it?", "Tell me the weather", "How are you?", "What is the capital of France?"]
      responses = ["It's 3 PM.", "The weather is sunny.", "I'm doing well, thank you.", "The capital of France is Paris."]
  
      # Tokenization
      tokenizer = Tokenizer()
      tokenizer.fit_on_texts(texts)
      sequences = tokenizer.texts_to_sequences(texts)
      max_len = max(len(seq) for seq in sequences)
      padded_sequences = pad_sequences(sequences, maxlen=max_len, padding='post')
  
      # Model creation
      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'))
  
      # Compile and train
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      model.fit(padded_sequences, np.array([0, 1, 2, 3]), epochs=10)
  
      # 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)
      response_index = np.argmax(prediction)
      
      return responses[response_index]