code: def _resnetv2(layers=(3, 4, 9), **kwargs):
      """ResNet-V2 backbone helper"""
      padding_same = kwargs.get("padding_same", True)
      stem_type = "same" if padding_same else ""
      conv_layer = (
          partial(StdConv2dSame, eps=1e-8)
          if padding_same
          else partial(StdConv2d, eps=1e-8)
      )
      if len(layers):
          backbone = ResNetV2(
              layers=layers,
              num_classes=0,
              global_pool="",
              in_chans=kwargs.get("in_chans", 3),
              preact=False,
              stem_type=stem_type,
              conv_layer=conv_layer,
          )
      else:
          backbone = create_resnetv2_stem(
              kwargs.get("in_chans", 3),
              stem_type=stem_type,
              preact=False,
              conv_layer=conv_layer,
          )
      return backbone
  
imports: from functools import partial
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import StdConv2dSame, StdConv2d, to_2tuple, Format, nchw_to
  from ._registry import generate_default_cfgs, register_model, register_model_deprecations
  from .resnet import resnet26d, resnet50d
  from .resnetv2 import ResNetV2, create_resnetv2_stem
  from .vision_transformer import _create_vision_transformer, VisionTransformer
label_desc: Use image node_id 2 for calling a global function with example usage: _resnetv2(layers) and returns: backbone