code: def swin_tiny_patch4_window7_224(num_classes: int = 1000, **kwargs):
      # trained ImageNet-1K
      # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth
      model = SwinTransformer(
          in_chans=3,
          patch_size=4,
          window_size=7,
          embed_dim=96,
          depths=(2, 2, 6, 2),
          num_heads=(3, 6, 12, 24),
          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 4 for calling a global function with example usage: swin_tiny_patch4_window7_224(num_classes) and returns: model