code: def x3d_xs(
      pretrained: bool = False,
      progress: bool = True,
      **kwargs,
  ):
      r"""
      X3D-XS model architecture [1] trained on the Kinetics dataset.
      Model with pretrained weights has top1 accuracy of 69.12.
  
      [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 the Kinetics dataset
          progress (bool): If True, displays a progress bar of the download to stderr
          kwargs: use these to modify any of the other model settings. All the
              options are defined in pytorchvideo/models/x3d.py
  
      NOTE: to use the pretrained model, do not modify the model configuration
      via the kwargs. Only modify settings via kwargs to initialize a new model
      without pretrained weights.
      """
      return _x3d(
          pretrained=pretrained,
          progress=progress,
          checkpoint_path=checkpoint_paths["x3d_xs"],
          input_clip_length=4,
          input_crop_size=160,
          **kwargs,
      )
  
file_path: pytorchvideo/pytorchvideo/models/hub/x3d.py
imports: from typing import Any, Optional
  import torch.nn
  from pytorchvideo.models.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: x3d_xs(pretrained, progress) and returns: _x3d