code: def omnivore_swinb(
      pretrained: bool = True,
      progress: bool = True,
      load_heads: bool = True,
      checkpoint_name: str = "omnivore_swinB",
      **kwargs: Any,
  ) -> nn.Module:
      r"""
      Omnivore model trunk: Swin B patch (2,4,4) window (1,6,7,7)
      Args:
          pretrained: if True loads weights from model trained on
              Imagenet 1k, Kinetics 400, 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
      """
  
      # Only specify the non default values
      trunk = VideoSwinTransformer(
          pretrained2d=False,
          patch_size=(2, 4, 4),
          embed_dim=128,
          depths=(2, 2, 18, 2),
          num_heads=(4, 8, 16, 32),
          window_size=(16, 7, 7),
          drop_path_rate=0.3,  # TODO: set this based on the final models
          patch_norm=True,  # Make this the default value?
          depth_mode="summed_rgb_d_tokens",
          **kwargs,
      )
  
      return _omnivore_base(
          trunk=trunk,
          head_dim_in=1024,  # embed_dim * 8 = 128*8
          progress=progress,
          pretrained=pretrained,
          load_heads=load_heads,
          checkpoint_name=checkpoint_name,
      )
  
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 7 for calling a global function with example usage: omnivore_swinb(pretrained, progress, load_heads, checkpoint_name) and returns: _omnivore_base