code: def SwinTransformer_base_patch4_window7_224(
      pretrained=False, **kwargs
  ):
      model = SwinTransformer(
          embed_dim=128,
          depths=[2, 2, 18, 2],
          num_heads=[4, 8, 16, 32],
          window_size=7,
          drop_path_rate=0.5,
          **kwargs,
      )
      if pretrained:
          load_pretrained_model(
              model,
              MODEL_URLS["SwinTransformer_base_patch4_window7_224"],
          )
      return model
  
imports: import numpy
  import paddle
  import paddle.nn
  from medicalseg.cvlibs import manager, param_init
  from medicalseg.utils import load_pretrained_model
  from medicalseg.models.backbones.transformer_utils import DropPath, to_2tuple
label_desc: Use image node_id 6 for calling a global function with example usage: SwinTransformer_base_patch4_window7_224(pretrained) and returns: model