code: def omnivore_swinb_epic(
      progress: bool = True,
      checkpoint_name: str = "omnivore_swinB_epic",
      **kwargs: Any,
  ) -> nn.Module:
      r"""
      Omnivore swin B model trained on EPIC-KITCHENS-100 dataset
      Args:
          progress: print progress of loading checkpoint
      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=0.4,
          patch_norm=True,
          depth_mode="summed_rgb_d_tokens",
          **kwargs,
      )
  
      heads = nn.Sequential(
          nn.Dropout(p=0.5),
          nn.Linear(in_features=1024, out_features=3806, bias=True),
      )
  
      return _omnivore_base(
          trunk=trunk,
          head_dim_in=1024,  # embed_dim * 8 = 128*8
          progress=progress,
          pretrained=True,
          load_heads=True,
          checkpoint_name=checkpoint_name,
          heads=heads,
      )
  
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 6 for calling a global function with example usage: omnivore_swinb_epic(progress, checkpoint_name) and returns: _omnivore_base