code: def resnet50_v1s(pretrained=False, **kwargs):
      model = ResNetV1b(
          BottleneckV1b,
          [3, 4, 6, 3],
          deep_stem=True,
          stem_width=64,
          **kwargs,
      )
      if pretrained:
          model_dict = model.state_dict()
          filtered_orig_dict = _safe_state_dict_filtering(
              torch.hub.load(
                  GLUON_RESNET_TORCH_HUB,
                  "gluon_resnet50_v1s",
                  pretrained=True,
              ).state_dict(),
              model_dict.keys(),
          )
          model_dict.update(filtered_orig_dict)
          model.load_state_dict(model_dict)
      return model
  
file_path: carefree-learn/cflearn/api/cv/third_party/iharm/model/modeling/resnetv1b.py
imports: import torch
  import torch.nn
label_desc: Use image node_id 3 for calling a global function with example usage: resnet50_v1s(pretrained) and returns: model