code: def load_from_pretrained(self):
      # TODO replace with fake torch load
  
      model_urls = {
          (
              18,
              1,
              64,
          ): "https://download.pytorch.org/models/resnet18-5c106cde.pth",
          (
              34,
              1,
              64,
          ): "https://download.pytorch.org/models/resnet34-333f7ec4.pth",
          (
              50,
              1,
              64,
          ): "https://download.pytorch.org/models/resnet50-19c8e357.pth",
          (
              50,
              32,
              4,
          ): "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth",
          (
              101,
              1,
              64,
          ): "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth",
          (
              152,
              1,
              64,
          ): "https://download.pytorch.org/models/resnet152-b121ed2d.pth",
      }
  
      self.url = model_urls[(self.num, self.groups, self.base_width)]
      for k, v in torch_load(fetch(self.url)).items():
          obj: Tensor = get_child(self, k)
          dat = v.detach().numpy()
  
          if "fc." in k and obj.shape != dat.shape:
              print("skipping fully connected layer")
              continue  # Skip FC if transfer learning
  
          # TODO: remove or when #777 is merged
          assert obj.shape == dat.shape or (
              obj.shape == (1,) and dat.shape == ()
          ), (k, obj.shape, dat.shape)
          obj.assign(dat)
  
file_path: tinygrad/extra/models/resnet.py
imports: import tinygrad.nn
  from tinygrad.tensor import Tensor
  from tinygrad.nn.state import torch_load
  from tinygrad.helpers import fetch, get_child
label_desc: Use image node_id 5 for calling the ResNet obj's underlying member method code with example usage: obj.load_from_pretrained() without return types