code: def build_model(cfg, gpu_id=None):
      """
      Builds the video model.
      Args:
          cfg (configs): configs that contains the hyper-parameters to build the
          backbone. Details can be seen in slowfast/config/defaults.py.
          gpu_id (Optional[int]): specify the gpu index to build model.
      """
      if torch.cuda.is_available():
          assert (
              cfg.NUM_GPUS <= torch.cuda.device_count()
          ), "Cannot use more GPU devices than available"
      else:
          assert (
              cfg.NUM_GPUS == 0
          ), "Cuda is not available. Please set `NUM_GPUS: 0 for running on CPUs."
  
      # Construct the model
      name = cfg.MODEL.MODEL_NAME
      model = MODEL_REGISTRY.get(name)(cfg)
  
      if cfg.BN.NORM_TYPE == "sync_batchnorm_apex":
          try:
              import apex
          except ImportError:
              raise ImportError(
                  "APEX is required for this model, pelase install"
              )
  
          logger.info("Converting BN layers to Apex SyncBN")
          process_group = apex.parallel.create_syncbn_process_group(
              group_size=cfg.BN.NUM_SYNC_DEVICES
          )
          model = apex.parallel.convert_syncbn_model(
              model, process_group=process_group
          )
  
      if cfg.NUM_GPUS:
          if gpu_id is None:
              # Determine the GPU used by the current process
              cur_device = torch.cuda.current_device()
          else:
              cur_device = gpu_id
          # Transfer the model to the current GPU device
          model = model.cuda(device=cur_device)
      # Use multi-process data parallel model in the multi-gpu setting
      if cfg.NUM_GPUS > 1:
          # Make model replica operate on the current device
          model = torch.nn.parallel.DistributedDataParallel(
              module=model,
              device_ids=[cur_device],
              output_device=cur_device,
              find_unused_parameters=True
              if cfg.MODEL.DETACH_FINAL_FC
              or cfg.MODEL.MODEL_NAME == "ContrastiveModel"
              else False,
          )
          if cfg.MODEL.FP16_ALLREDUCE:
              model.register_comm_hook(
                  state=None, hook=comm_hooks_default.fp16_compress_hook
              )
      return model
  
imports: import torch
  from fvcore.common.registry import Registry
  from torch.distributed.algorithms.ddp_comm_hooks import default
  import slowfast.utils.logging
label_desc: Use image node_id 1 for calling a global function with example usage: build_model(cfg, gpu_id) and returns: model