code: def edgenext_x_small(pretrained=False, **kwargs) -> EdgeNeXt:
      # 2.34M & 538.0M @ 256 resolution
      # 75.00% Top-1 accuracy
      # No AA, No Mixup & Cutmix, DropPath=0.0, BS=4096, lr=0.006, multi-scale-sampler
      # Jetson FPS=31.61 versus 28.49 for MobileViT_XS
      # For A100: FPS @ BS=1: 179.55 & @ BS=256: 4404.95 versus FPS @ BS=1: 94.55 & @ BS=256: 2361.53 for MobileViT_XS
      model_args = dict(
          depths=(3, 3, 9, 3),
          dims=(32, 64, 100, 192),
          heads=(4, 4, 4, 4),
      )
      return _create_edgenext(
          "edgenext_x_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 6 for calling a global function with example usage: edgenext_x_small(pretrained) and returns: _create_edgenext