code: def gpu_is_blocked(q, gpu_id):
      experiments = get_experiments(q=q)
      running_experiments = experiments[
          experiments.status.isin(["running"])
      ]
      gpu_blocked = any(
          [
              str(gpu_id) in gpu_list
              for gpu_list in running_experiments["gpu_list"]
              .apply(lambda x: x.split(","))
              .to_list()
          ]
      )
      return gpu_blocked
  
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 1 for calling a global function with example usage: gpu_is_blocked(q, gpu_id) and returns: gpu_blocked