code: def resnet34_v1b(pretrained=False, **kwargs):
      model = ResNetV1b(BasicBlockV1b, [3, 4, 6, 3], **kwargs)
      if pretrained:
          model_dict = model.state_dict()
          filtered_orig_dict = _safe_state_dict_filtering(
              torch.hub.load(
                  GLUON_RESNET_TORCH_HUB,
                  "gluon_resnet34_v1b",
                  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 2 for calling a global function with example usage: resnet34_v1b(pretrained) and returns: model