code: def s3d(
      *,
      weights: Optional[S3D_Weights] = None,
      progress: bool = True,
      **kwargs: Any,
  ) -> S3D:
      """Construct Separable 3D CNN model.
  
      Reference: `Rethinking Spatiotemporal Feature Learning <https://arxiv.org/abs/1712.04851>`__.
  
      .. betastatus:: video module
  
      Args:
          weights (:class:`~torchvision.models.video.S3D_Weights`, optional): The
              pretrained weights to use. See
              :class:`~torchvision.models.video.S3D_Weights`
              below for more details, and possible values. By default, no
              pre-trained weights are used.
          progress (bool): If True, displays a progress bar of the download to stderr. Default is True.
          **kwargs: parameters passed to the ``torchvision.models.video.S3D`` base class.
              Please refer to the `source code
              <https://github.com/pytorch/vision/blob/main/torchvision/models/video/s3d.py>`_
              for more details about this class.
  
      .. autoclass:: torchvision.models.video.S3D_Weights
          :members:
      """
      weights = S3D_Weights.verify(weights)
  
      if weights is not None:
          _ovewrite_named_param(
              kwargs, "num_classes", len(weights.meta["categories"])
          )
  
      model = S3D(**kwargs)
  
      if weights is not None:
          model.load_state_dict(
              weights.get_state_dict(progress=progress, check_hash=True)
          )
  
      return model
  
imports: from functools import partial
  from typing import Any, Callable, Optional
  import torch
  from torch import nn
  from torchvision.ops.misc import Conv3dNormActivation
  from ...transforms._presets import VideoClassification
  from ...utils import _log_api_usage_once
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _KINETICS400_CATEGORIES
  from .._utils import _ovewrite_named_param, handle_legacy_interface
label_desc: Use image node_id 1 for calling a global function with example usage: s3d() and returns: model