code: def edgenext_base(pretrained=False, **kwargs) -> EdgeNeXt:
      # 18.51M & 3840.93M @ 256 resolution
      # 82.5% (normal) 83.7% (USI) Top-1 accuracy
      # AA=True, Mixup & Cutmix, DropPath=0.1, BS=4096, lr=0.006, multi-scale-sampler
      # Jetson FPS=xx.xx versus xx.xx for MobileViT_S
      # For A100: FPS @ BS=1: xxx.xx & @ BS=256: xxxx.xx
      model_args = dict(depths=[3, 3, 9, 3], dims=[80, 160, 288, 584])
      return _create_edgenext(
          "edgenext_base",
          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 8 for calling a global function with example usage: edgenext_base(pretrained) and returns: _create_edgenext