code: def test_training(self):
      if not self.model_tester.is_training:
          return
  
      config, *inputs = self.model_tester.prepare_config_and_inputs()
      (
          input_ids,
          token_type_ids,
          input_mask,
          scorer_encoder_inputs,
      ) = inputs[0:4]
      config.return_dict = True
  
      tokenizer = RealmTokenizer.from_pretrained(
          "google/realm-orqa-nq-openqa"
      )
  
      # RealmKnowledgeAugEncoder training
      model = RealmKnowledgeAugEncoder(config)
      model.to(torch_device)
      model.train()
  
      inputs_dict = {
          "input_ids": scorer_encoder_inputs[0].to(torch_device),
          "attention_mask": scorer_encoder_inputs[1].to(torch_device),
          "token_type_ids": scorer_encoder_inputs[2].to(torch_device),
          "relevance_score": floats_tensor(
              [
                  self.model_tester.batch_size,
                  self.model_tester.num_candidates,
              ]
          ),
      }
      inputs_dict["labels"] = torch.zeros(
          (self.model_tester.batch_size, self.model_tester.seq_length),
          dtype=torch.long,
          device=torch_device,
      )
      inputs = inputs_dict
      loss = model(**inputs).loss
      loss.backward()
  
      # RealmForOpenQA training
      openqa_config = copy.deepcopy(config)
      openqa_config.vocab_size = 30522  # the retrieved texts will inevitably have more than 99 vocabs.
      openqa_config.num_block_records = 5
      openqa_config.searcher_beam_size = 2
  
      block_records = np.array(
          [
              b"This is the first record.",
              b"This is the second record.",
              b"This is the third record.",
              b"This is the fourth record.",
              b"This is the fifth record.",
          ],
          dtype=np.object,
      )
      retriever = RealmRetriever(block_records, tokenizer)
      model = RealmForOpenQA(openqa_config, retriever)
      model.to(torch_device)
      model.train()
  
      inputs_dict = {
          "input_ids": input_ids[:1].to(torch_device),
          "attention_mask": input_mask[:1].to(torch_device),
          "token_type_ids": token_type_ids[:1].to(torch_device),
          "answer_ids": input_ids[:1].tolist(),
      }
      inputs = self._prepare_for_class(inputs_dict, RealmForOpenQA)
      loss = model(**inputs).reader_output.loss
      loss.backward()
  
      # Test model.block_embedding_to
      device = torch.device("cpu")
      model.block_embedding_to(device)
      loss = model(**inputs).reader_output.loss
      loss.backward()
      self.assertEqual(model.block_emb.device.type, device.type)
  
imports: import copy
  import unittest
  import numpy
  from transformers import RealmConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 7 for calling the RealmModelTest obj's underlying member method code with example usage: obj.test_training() without return types