code: def mixer_s16_224(pretrained=False, **kwargs) -> MlpMixer:
      """Mixer-S/16 224x224
      Paper:  'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
      """
      model_args = dict(
          patch_size=16, num_blocks=8, embed_dim=512, **kwargs
      )
      model = _create_mixer(
          "mixer_s16_224", pretrained=pretrained, **model_args
      )
      return model
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, GluMlp, GatedMlp, DropPath, lecun_normal_, to_2tuple
  from ._builder import build_model_with_cfg
  from ._manipulate import named_apply, checkpoint_seq
  from ._registry import generate_default_cfgs, register_model, register_model_deprecations
label_desc: Use image node_id 6 for calling a global function with example usage: mixer_s16_224(pretrained) and returns: model