code: def resmlp_24_224(pretrained=False, **kwargs) -> MlpMixer:
      """ResMLP-24
      Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
      """
      model_args = dict(
          patch_size=16,
          num_blocks=24,
          embed_dim=384,
          mlp_ratio=4,
          block_layer=partial(ResBlock, init_values=1e-5),
          norm_layer=Affine,
          **kwargs,
      )
      model = _create_mixer(
          "resmlp_24_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 14 for calling a global function with example usage: resmlp_24_224(pretrained) and returns: model