code: def set_env_variables(gpu_id):
      # Disable tensorflow logs, except in the case of an error.
      if "TF_CPP_MIN_LOG_LEVEL" not in os.environ:
          os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
  
      # Set sharing strategy to file_system to avoid file descriptor leaks
      torch.multiprocessing.set_sharing_strategy("file_system")
  
      # Only use one GPU, if we have one.
      # For Tensorflow
      # TODO: Using USE with `--parallel` raises similar issue as https://github.com/tensorflow/tensorflow/issues/38518#
      os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
      # For PyTorch
      torch.cuda.set_device(gpu_id)
  
      # Fix TensorFlow GPU memory growth
      try:
          import tensorflow as tf
  
          gpus = tf.config.experimental.list_physical_devices("GPU")
          if gpus:
              try:
                  # Currently, memory growth needs to be the same across GPUs
                  gpu = gpus[gpu_id]
                  tf.config.experimental.set_visible_devices(gpu, "GPU")
                  tf.config.experimental.set_memory_growth(gpu, True)
              except RuntimeError as e:
                  print(e)
      except ModuleNotFoundError:
          pass
  
imports: import collections
  import logging
  import multiprocessing
  import os
  import queue
  import random
  import traceback
  import torch
  import tqdm
  import textattack
  from textattack.attack_results import FailedAttackResult, MaximizedAttackResult, SkippedAttackResult, SuccessfulAttackResult
  from textattack.shared.utils import logger
  from .attack import Attack
  from .attack_args import AttackArgs
label_desc: Use image node_id 2 for calling a global function with example usage: set_env_variables(gpu_id) without return types