code: def test_questions(self):
      from extra.models.bert import BertForQuestionAnswering
      from transformers import (
          BertForQuestionAnswering as TorchBertForQuestionAnswering,
      )
      from transformers import BertConfig
  
      # small
      config = {
          "vocab_size": 24,
          "hidden_size": 2,
          "num_hidden_layers": 2,
          "num_attention_heads": 2,
          "intermediate_size": 32,
          "hidden_dropout_prob": 0.1,
          "attention_probs_dropout_prob": 0.1,
          "max_position_embeddings": 512,
          "type_vocab_size": 2,
      }
  
      # Create in tinygrad
      Tensor.manual_seed(1337)
      mdl = BertForQuestionAnswering(**config)
  
      # Create in torch
      with torch.no_grad():
          torch_mdl = TorchBertForQuestionAnswering(
              BertConfig(**config)
          )
  
      set_equal_weights(mdl, torch_mdl)
  
      seeds = (1337, 3141)
      bsz, seq_len = 1, 16
      for _, seed in enumerate(seeds):
          in_ids, mask, seg_ids = get_question_samp(
              bsz, seq_len, config["vocab_size"], seed
          )
          out = mdl(Tensor(in_ids), Tensor(mask), Tensor(seg_ids))
          torch_out = torch_mdl.forward(
              torch.from_numpy(in_ids).long(),
              torch.from_numpy(mask),
              torch.from_numpy(seg_ids).long(),
          )[:2]
          torch_out = torch.cat(torch_out).unsqueeze(2)
          np.testing.assert_allclose(
              out.numpy(),
              torch_out.detach().numpy(),
              atol=5e-4,
              rtol=5e-4,
          )
  
file_path: tinygrad/test/models/test_bert.py
imports: import unittest
  import numpy
  from tinygrad.tensor import Tensor
  import torch
label_desc: Use image node_id 1 for calling the TestBert obj's underlying member method code with example usage: obj.test_questions() without return types