code: def initialize_device_settings(use_cuda, local_rank=-1, use_amp=None):
      if not use_cuda:
          device = torch.device("cpu")
          n_gpu = 0
      elif local_rank == -1:
          device = torch.device(
              "cuda" if torch.cuda.is_available() else "cpu"
          )
          if not torch.cuda.is_available():
              n_gpu = 0
          else:
              n_gpu = torch.cuda.device_count()
      else:
          device = torch.device("cuda", local_rank)
          torch.cuda.set_device(device)
          n_gpu = 1
          # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
          torch.distributed.init_process_group(backend="nccl")
      logger.info(f"Using device: {str(device).upper()} ")
      logger.info(f"Number of GPUs: {n_gpu}")
      logger.info(f"Distributed Training: {bool(local_rank != -1)}")
      logger.info(f"Automatic Mixed Precision: {use_amp}")
      return device, n_gpu
  
imports: import hashlib
  import json
  import logging
  import random
  import os
  import signal
  import numpy
  import torch
  import torch.distributed
  from requests.exceptions import ConnectionError
  from torch import multiprocessing
  import mlflow
  from copy import deepcopy
  import pandas
  from tqdm import tqdm
  import time
  import pickle
  from farm.visual.ascii.images import WELCOME_BARN, WORKER_M, WORKER_F, WORKER_X
label_desc: Use image node_id 3 for calling a global function with example usage: initialize_device_settings(use_cuda, local_rank, use_amp) and returns: device,  n_gpu