code: def deploy_model(model, cfg):
      """
      Deploy model to multiple GPUs for DDP training.
      """
      if cfg.DDP_CONFIG.DISTRIBUTED:
          if cfg.DDP_CONFIG.GPU is not None:
              torch.cuda.set_device(cfg.DDP_CONFIG.GPU)
              model.cuda(cfg.DDP_CONFIG.GPU)
              model = torch.nn.parallel.DistributedDataParallel(
                  model,
                  device_ids=[cfg.DDP_CONFIG.GPU],
                  find_unused_parameters=True,
              )
          else:
              model.cuda()
              model = torch.nn.parallel.DistributedDataParallel(
                  model, find_unused_parameters=True
              )
      elif cfg.DDP_CONFIG.GPU is not None:
          torch.cuda.set_device(cfg.DDP_CONFIG.GPU)
          model.cuda(cfg.DDP_CONFIG.GPU)
      else:
          # DataParallel will divide and allocate batch_size to all available GPUs
          model = torch.nn.DataParallel(model).cuda()
  
      return model
  
file_path: gluon-cv/gluoncv/torch/utils/model_utils.py
imports: import os
  import hashlib
  import requests
  from tqdm import tqdm
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: deploy_model(model, cfg) and returns: model