code: def _create_convnext(variant, pretrained=False, **kwargs):
      if kwargs.get("pretrained_cfg", "") == "fcmae":
          # NOTE fcmae pretrained weights have no classifier or final norm-layer (`head.norm`)
          # This is workaround loading with num_classes=0 w/o removing norm-layer.
          kwargs.setdefault("pretrained_strict", False)
  
      model = build_model_with_cfg(
          ConvNeXt,
          variant,
          pretrained,
          pretrained_filter_fn=checkpoint_filter_fn,
          feature_cfg=dict(
              out_indices=(0, 1, 2, 3), flatten_sequential=True
          ),
          **kwargs,
      )
      return model
  
imports: from collections import OrderedDict
  from functools import partial
  from typing import Callable, Optional, Tuple, Union
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
  from timm.layers import trunc_normal_, AvgPool2dSame, DropPath, Mlp, GlobalResponseNormMlp, LayerNorm2d, LayerNorm, create_conv2d, get_act_layer, make_divisible, to_ntuple
  from timm.layers import NormMlpClassifierHead, ClassifierHead
  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 3 for calling a global function with example usage: _create_convnext(variant, pretrained) and returns: model