code: def _efficient_x3d(
      pretrained: bool = False,
      progress: bool = True,
      checkpoint_path: Optional[str] = None,
      # Model params
      expansion: str = "XS",
      **kwargs: Any,
  ) -> nn.Module:
      model = create_x3d(
          expansion=expansion,
          **kwargs,
      )
  
      if pretrained and checkpoint_path is not None:
          # All models are loaded onto CPU by default
          state_dict = load_state_dict_from_url(
              checkpoint_path, progress=progress, map_location="cpu"
          )
          model.load_state_dict(state_dict)
  
      return model
  
imports: from typing import Any, Optional
  import torch.nn
  from pytorchvideo.models.accelerator.mobile_cpu.efficient_x3d import create_x3d
  from torch.hub import load_state_dict_from_url
label_desc: Use image node_id 1 for calling a global function with example usage: _efficient_x3d(pretrained, progress, checkpoint_path, expansion) and returns: model