code: def omnivore_swinl_kinetics600(
      pretrained: bool = True,
      progress: bool = True,
      load_heads: bool = True,
      **kwargs: Any,
  ) -> nn.Module:
      r"""
      Swin L patch 244 window 877 trained with Kinetics 600
      Args:
          pretrained: if True loads weights from model trained on
              Imagenet 1k, Kinetics 600, SUN RGBD.
          progress: print progress of loading checkpoint
          load_heads: if True, loads the 3 heads, one each for
              image/video/rgbd prediction. If False loads only the
              trunk.
      Returns:
          model: nn.Module of the omnivore model
      """
  
      heads = nn.ModuleDict(
          {
              "image": get_imagenet_head(dim_in=1536),
              "rgbd": get_sunrgbd_head(dim_in=1536),
              "video": get_kinetics_head(dim_in=1536, num_classes=600),
          }
      )
  
      return _omnivore_swinl(
          pretrained=pretrained,
          progress=progress,
          load_heads=load_heads,
          checkpoint_name="omnivore_swinL_kinetics600",
          heads=heads,
          **kwargs,
      )
  
imports: from towhee.models.video_swin_transformer.video_swin_transformer import VideoSwinTransformer
  from typing import Any, Optional, Union
  import torch
  from torch import nn
  from torch.hub import load_state_dict_from_url
label_desc: Use image node_id 13 for calling a global function with example usage: omnivore_swinl_kinetics600(pretrained, progress, load_heads) and returns: _omnivore_swinl