code: def load_from_pretrained(self):
      fn = Path(__file__).parents[1] / "weights/bert_for_qa.pt"
      fetch(
          "https://zenodo.org/record/3733896/files/model.pytorch?download=1",
          fn,
      )
      fn_vocab = Path(__file__).parents[1] / "weights/bert_vocab.txt"
      fetch(
          "https://zenodo.org/record/3733896/files/vocab.txt?download=1",
          fn_vocab,
      )
  
      import torch
  
      with open(fn, "rb") as f:
          state_dict = torch.load(f, map_location="cpu")
  
      for k, v in state_dict.items():
          if "dropout" in k:
              continue  # skip dropout
          if "pooler" in k:
              continue  # skip pooler
          get_child(self, k).assign(v.numpy()).realize()
  
imports: from tinygrad.tensor import Tensor
  from tinygrad.nn import Linear, LayerNorm, Embedding
  from tinygrad.helpers import fetch, get_child
  from pathlib import Path
label_desc: Use image node_id 2 for calling the BertForQuestionAnswering obj's underlying member method code with example usage: obj.load_from_pretrained() without return types