code: def main(opts, **kwargs):
      dev_id = getattr(opts, "dev.device_id", torch.device("cpu"))
      device = getattr(opts, "dev.device", torch.device("cpu"))
      use_distributed = getattr(opts, "ddp.use_distributed")
      is_master_node = is_master(opts)
  
      # set-up data loaders
      test_loader = create_test_loader(opts)
  
      # set-up the model
      model = get_model(opts)
      if is_master_node:
          # print model information
          model.info()
  
      # memory format
      memory_format = (
          torch.channels_last
          if getattr(opts, "common.channels_last")
          else torch.contiguous_format
      )
      model = model.to(device=device, memory_format=memory_format)
  
      if use_distributed:
          model = model.to(device=device, memory_format=memory_format)
          model = torch.nn.parallel.DistributedDataParallel(
              model, device_ids=[dev_id], output_device=dev_id
          )
          if is_master_node:
              logger.log("Using DistributedDataParallel for evaluation")
  
      eval_engine = Evaluator(
          opts=opts, model=model, test_loader=test_loader
      )
      eval_engine.run()
  
file_path: ml-cvnets/main_eval.py
imports: from typing import List, Optional
  import torch
  from cvnets import get_model
  from data import create_test_loader
  from engine import Evaluator
  from options.opts import get_eval_arguments
  from utils import logger, resources
  from utils.common_utils import create_directories, device_setup
  from utils.ddp_utils import distributed_init, is_master
label_desc: Use image node_id 1 for calling a global function with example usage: main(opts) without return types