code: def convnextv2_large(pretrained=False, **kwargs) -> ConvNeXt:
      model_args = dict(
          depths=[3, 3, 27, 3],
          dims=[192, 384, 768, 1536],
          use_grn=True,
          ls_init_value=None,
      )
      model = _create_convnext(
          "convnextv2_large",
          pretrained=pretrained,
          **dict(model_args, **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 29 for calling a global function with example usage: convnextv2_large(pretrained) and returns: model