code: def swin_base_patch4_window12_384_in22k(
      num_classes: int = 21841, **kwargs
  ):
      # trained ImageNet-22K
      # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth
      model = SwinTransformer(
          in_chans=3,
          patch_size=4,
          window_size=12,
          embed_dim=128,
          depths=(2, 2, 18, 2),
          num_heads=(4, 8, 16, 32),
          num_classes=num_classes,
          **kwargs,
      )
      return model
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  import numpy
  from typing import Optional
label_desc: Use image node_id 9 for calling a global function with example usage: swin_base_patch4_window12_384_in22k(num_classes) and returns: model