code: def resnet_jmlr(pretrained=False, **kwargs):
      model_args = dict(
          block=BasicBlock,
          layers=[5, 3, 4, 2],
          stem_width=32,
          stem_type="deep",
          avg_down=True,
          channels=[64, 160, 272, 512],
          **kwargs,
      )
      # return _create_resnet('resnet34d', pretrained, **model_args)
      return ResNet(**model_args)
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.models.helpers import build_model_with_cfg
  from timm.models.layers import DropBlock2d, DropPath, AvgPool2dSame, BlurPool2d, GroupNorm, create_attn, get_attn, create_classifier
label_desc: Use image node_id 8 for calling a global function with example usage: resnet_jmlr(pretrained) and returns: ResNet