code: def resnetv2_50d_frn(pretrained=False, **kwargs) -> ResNetV2:
      model_args = dict(
          layers=[3, 4, 6, 3],
          conv_layer=create_conv2d,
          norm_layer=FilterResponseNormTlu2d,
          stem_type="deep",
          avg_down=True,
      )
      return _create_resnetv2(
          "resnetv2_50d_frn",
          pretrained=pretrained,
          **dict(model_args, **kwargs),
      )
  
imports: from collections import OrderedDict
  from functools import partial
  import torch
  import torch.nn
  from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from timm.layers import GroupNormAct, BatchNormAct2d, EvoNorm2dS0, FilterResponseNormTlu2d, ClassifierHead, DropPath, AvgPool2dSame, create_pool2d, StdConv2d, create_conv2d, get_act_layer, get_norm_act_layer, make_divisible
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq, named_apply, adapt_input_conv
  from ._registry import generate_default_cfgs, register_model, register_model_deprecations
label_desc: Use image node_id 23 for calling a global function with example usage: resnetv2_50d_frn(pretrained) and returns: _create_resnetv2