docstr: Create the answer by joining the generated words.
  By this, self.text_cleaner does not need to be idempotent.
docstr_tok: ["Create","the","answer","by","joining","the","generated","words",".","By","this",",","self.text_cleaner","does","not","need","to","be","idempotent","."]
code: def answer(self):
      """
      Create the answer by joining the generated words.
      By this, self.text_cleaner does not need to be idempotent.
      """
      answer = "".join(self.words_predicted_answer)
      if answer.endswith(self.tokenizer.eos_token):
          # text generation is stopped
          answer = answer.replace(self.tokenizer.eos_token, "")
      if self.text_cleaner:
          answer = self.text_cleaner(answer)
      return answer
  
imports: import asyncio
  import gc
  import logging
  import os
  import threading
  from typing import Any, Callable, Dict, List, Optional
  import numpy
  import torch
  from accelerate import dispatch_model, infer_auto_device_map
  from accelerate.utils import get_balanced_memory
  from h2o_wave import Q
  from h2o_wave import data
  from h2o_wave import ui
  from transformers import AutoTokenizer, TextStreamer
  from llm_studio.app_utils.utils import get_experiments, get_ui_elements, parse_ui_elements, set_env
  from llm_studio.python_configs.base import DefaultConfigProblemBase
  from llm_studio.src.datasets.text_utils import get_tokenizer
  from llm_studio.src.models.text_causal_language_modeling_model import Model
  from llm_studio.src.utils.config_utils import NON_GENERATION_PROBLEM_TYPES, load_config_yaml
  from llm_studio.src.utils.modeling_utils import load_checkpoint
label_desc: Use image node_id 3 for calling the WaveChatStreamer obj's underlying member method code with example usage: obj.answer() and returns: answer