code: def edgenext_small_rw(pretrained=False, **kwargs) -> EdgeNeXt:
      model_args = dict(
          depths=(3, 3, 9, 3),
          dims=(48, 96, 192, 384),
          downsample_block=True,
          conv_bias=False,
          stem_type="overlap",
      )
      return _create_edgenext(
          "edgenext_small_rw",
          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 9 for calling a global function with example usage: edgenext_small_rw(pretrained) and returns: _create_edgenext