input: Write a function to create a Python system for generating natural language responses using TensorFlow and Keras. The function should take a sample utterance as input, build a model with appropriate layers, compile the model, train it, and then predict the response to the sample utterance. The function should return the predicted response.
  assert generate_response("What time is it?")==prediction
  model.fit(inputs, outputs, epochs=10, batch_size=32)
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
  
      # Sample data
      inputs = ["What time is it?", "How are you?", "What's the weather?"]
      outputs = ["It's 3 PM.", "I'm fine, thank you.", "It's sunny."]
  
      # Tokenize the data
      tokenizer = Tokenizer()
      tokenizer.fit_on_texts(inputs + outputs)
      input_sequences = tokenizer.texts_to_sequences([utterance])
      input_padded = pad_sequences(input_sequences, maxlen=10)
  
      # Prepare training data
      train_inputs = tokenizer.texts_to_sequences(inputs)
      train_inputs_padded = pad_sequences(train_inputs, maxlen=10)
      train_outputs = tokenizer.texts_to_sequences(outputs)
      train_outputs_padded = pad_sequences(train_outputs, maxlen=10)
  
      # Build the model
      model = Sequential()
      model.add(Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=64, input_length=10))
      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(train_inputs_padded, train_outputs_padded, epochs=10, batch_size=32)
  
      # Predict the response
      prediction = model.predict(input_padded)
      predicted_sequence = [tf.argmax(pred, axis=1).numpy()[0] for pred in prediction]
      predicted_response = tokenizer.sequences_to_texts([predicted_sequence])[0]
  
      return predicted_response