code: def load_from_pretrained(self):
      model_urls = {
          (
              50,
              1,
              64,
          ): "https://download.pytorch.org/models/retinanet_resnet50_fpn_coco-eeacb38b.pth",
          (
              50,
              32,
              4,
          ): "https://zenodo.org/record/6605272/files/retinanet_model_10.zip",
      }
      self.url = model_urls[
          (
              self.backbone.body.num,
              self.backbone.body.groups,
              self.backbone.body.base_width,
          )
      ]
      from torch.hub import load_state_dict_from_url
  
      state_dict = load_state_dict_from_url(
          self.url, progress=True, map_location="cpu"
      )
      state_dict = (
          state_dict["model"]
          if "model" in state_dict.keys()
          else state_dict
      )
      for k, v in state_dict.items():
          obj = get_child(self, k)
          dat = v.detach().numpy()
          assert obj.shape == dat.shape, (k, obj.shape, dat.shape)
          obj.assign(dat)
  
imports: import math
  from tinygrad.helpers import flatten, get_child
  import tinygrad.nn
  from extra.models.resnet import ResNet
  import numpy
label_desc: Use image node_id 4 for calling the RetinaNet obj's underlying member method code with example usage: obj.load_from_pretrained() without return types