code: def bot(history):
      user_message = history[-1][0]
      new_user_input_ids = tokenizer.encode(
          user_message + tokenizer.eos_token, return_tensors="pt"
      )
  
      # append the new user input tokens to the chat history
      bot_input_ids = torch.cat(
          [torch.LongTensor([]), new_user_input_ids], dim=-1
      )
  
      # generate a response
      response = model.generate(
          bot_input_ids,
          max_length=1000,
          pad_token_id=tokenizer.eos_token_id,
      ).tolist()
  
      # convert the tokens to text, and then split the responses into lines
      response = tokenizer.decode(response[0]).split("<|endoftext|>")
      response = [
          (response[i], response[i + 1])
          for i in range(0, len(response) - 1, 2)
      ]  # convert to tuples of list
      history[-1] = response[0]
      return history
  
imports: import gradio
  from transformers import AutoModelForCausalLM, AutoTokenizer
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: bot(history) and returns: history