code: def edgenext_small(pretrained=False, **kwargs) -> EdgeNeXt:
      # 5.59M & 1260.59M @ 256 resolution
      # 79.43% Top-1 accuracy
      # AA=True, No Mixup & Cutmix, DropPath=0.1, BS=4096, lr=0.006, multi-scale-sampler
      # Jetson FPS=20.47 versus 18.86 for MobileViT_S
      # For A100: FPS @ BS=1: 172.33 & @ BS=256: 3010.25 versus FPS @ BS=1: 93.84 & @ BS=256: 1785.92 for MobileViT_S
      model_args = dict(depths=(3, 3, 9, 3), dims=(48, 96, 160, 304))
      return _create_edgenext(
          "edgenext_small",
          pretrained=pretrained,
          **dict(model_args, **kwargs),
      )
  
imports: import math
  from collections import OrderedDict
  from functools import partial
  from typing import Tuple
  import torch
  import torch.nn.functional
  from torch import nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import trunc_normal_tf_, DropPath, LayerNorm2d, Mlp, SelectAdaptivePool2d, create_conv2d, use_fused_attn, NormMlpClassifierHead, ClassifierHead
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_module
  from ._manipulate import named_apply, checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 7 for calling a global function with example usage: edgenext_small(pretrained) and returns: _create_edgenext