code: def efficient_x3d_xs(
      pretrained: bool = False, progress: bool = True, **kwargs
  ):
      r"""
      X3D-XS model architectures [1] with pretrained weights trained
      on the Kinetics dataset with efficient implementation for mobile cpu.
  
      [1] Christoph Feichtenhofer, "X3D: Expanding Architectures for
      Efficient Video Recognition." https://arxiv.org/abs/2004.04730
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on Kinetcis-400 dataset
          progress (bool): If True, displays a progress bar of the download to stderr
          To modify any other model settings, specify them in the kwargs.
          All the args are defined in pytorchvideo/models/x3d.py
      """
      return _efficient_x3d(
          pretrained=pretrained,
          progress=progress,
          checkpoint_path=_checkpoint_paths["efficient_x3d_xs"],
          expansion="XS",
          **kwargs,
      )
  
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 2 for calling a global function with example usage: efficient_x3d_xs(pretrained, progress) and returns: _efficient_x3d