code: def load_from_pretrained(self):
      fn = Path(__file__).parents[1] / "weights" / "unet-3d.ckpt"
      fetch(
          "https://zenodo.org/record/5597155/files/3dunet_kits19_pytorch.ptc?download=1",
          fn,
      )
      state_dict = torch.jit.load(
          fn, map_location=torch.device("cpu")
      ).state_dict()
      for k, v in state_dict.items():
          obj = get_child(self, k)
          assert obj.shape == v.shape, (k, obj.shape, v.shape)
          obj.assign(v.numpy())
  
file_path: tinygrad/extra/models/unet3d.py
imports: from pathlib import Path
  import torch
  from tinygrad import nn
  from tinygrad.tensor import Tensor
  from tinygrad.helpers import fetch, get_child
label_desc: Use image node_id 3 for calling the UNet3D obj's underlying member method code with example usage: obj.load_from_pretrained() without return types