code: def poolformer_s36(pretrained=False, **kwargs):
      """
      PoolFormer-S36 model, Params: 31M
      """
      layers = [6, 6, 18, 6]
      embed_dims = [64, 128, 320, 512]
      mlp_ratios = [4, 4, 4, 4]
      downsamples = [True, True, True, True]
      model = PoolFormer(
          layers,
          embed_dims=embed_dims,
          mlp_ratios=mlp_ratios,
          downsamples=downsamples,
          layer_scale_init_value=1e-6,
          **kwargs,
      )
      model.default_cfg = default_cfgs["poolformer_s"]
      if pretrained:
          url = model_urls["poolformer_s36"]
          checkpoint = torch.hub.load_state_dict_from_url(
              url=url, map_location="cpu", check_hash=True
          )
          model.load_state_dict(checkpoint)
      return model
  
imports: from torch import nn
  import torch
  import os
  import copy
  from towhee.models.poolformer.patchembed import PatchEmbed
  from towhee.models.utils.weight_init import trunc_normal_
  from towhee.models.poolformer.basic_blocks import basic_blocks
  from towhee.models.poolformer.groupnorm import GroupNorm
label_desc: Use image node_id 4 for calling a global function with example usage: poolformer_s36(pretrained) and returns: model