code: def SeaFormer_large(pretrained, **kwags):
      seaformer = SeaFormer(
          pretrained=pretrained,
          cfgs=[
              [[3, 3, 32, 1], [3, 4, 64, 2], [3, 4, 64, 1]],
              [[5, 4, 128, 2], [5, 4, 128, 1]],
              [[3, 4, 192, 2], [3, 4, 192, 1]],
              [[5, 4, 256, 2]],
              [[3, 6, 320, 2]],
          ],
          emb_dims=[192, 256, 320],
          key_dims=[16, 20, 24],
          channels=[32, 64, 128, 192, 256, 320],
          depths=[3, 3, 3],
          num_heads=8,
          mlp_ratios=[2, 4, 6],
          **kwags,
      )
      seaformer.feat_channels = [128, 192, 256, 320]
  
      return seaformer
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
  from paddleseg.models.backbones.transformer_utils import DropPath, zeros_, ones_
  from paddleseg.utils import utils
label_desc: Use image node_id 5 for calling a global function with example usage: SeaFormer_large(pretrained) and returns: seaformer