code: def generate(
      model: Model,
      inputs: Dict,
      cfg: Any,
      streamer: TextStreamer = None,
  ):
      with torch.cuda.amp.autocast():
          output = (
              model.generate(batch=inputs, cfg=cfg, streamer=streamer)
              .detach()
              .cpu()
          )
      return output
  
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 2 for calling a global function with example usage: generate(model, inputs, cfg, streamer) and returns: output