code: def test_torch_encode_plus_sent_to_model(self):
      import torch
  
      from transformers import MODEL_MAPPING, TOKENIZER_MAPPING
  
      MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(
          MODEL_MAPPING, TOKENIZER_MAPPING
      )
  
      tokenizers = self.get_tokenizers(do_lower_case=False)
      for tokenizer in tokenizers:
          with self.subTest(f"{tokenizer.__class__.__name__}"):
              if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING:
                  return
  
              config_class, model_class = MODEL_TOKENIZER_MAPPING[
                  tokenizer.__class__
              ]
              config = config_class()
  
              if (
                  config.is_encoder_decoder
                  or config.pad_token_id is None
              ):
                  return
  
              model = model_class(config)
  
              # The following test is different from the common's one
              self.assertGreaterEqual(
                  model.bert_query.get_input_embeddings().weight.shape[
                      0
                  ],
                  len(tokenizer),
              )
  
              # Build sequence
              first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
              sequence = " ".join(first_ten_tokens)
              encoded_sequence = tokenizer.encode_plus(
                  sequence, return_tensors="pt"
              )
  
              # Ensure that the BatchEncoding.to() method works.
              encoded_sequence.to(model.device)
  
              batch_encoded_sequence = tokenizer.batch_encode_plus(
                  [sequence, sequence], return_tensors="pt"
              )
              # This should not fail
  
              with torch.no_grad():  # saves some time
                  # The following lines are different from the common's ones
                  model.embed_questions(**encoded_sequence)
                  model.embed_questions(**batch_encoded_sequence)
  
imports: import os
  import unittest
  from transformers import RetriBertTokenizer, RetriBertTokenizerFast
  from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BasicTokenizer, WordpieceTokenizer, _is_control, _is_punctuation, _is_whitespace
  from transformers.testing_utils import require_tokenizers, require_torch, slow
  from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english, merge_model_tokenizer_mappings
label_desc: Use image node_id 22 for calling the RetriBertTokenizationTest obj's underlying member method code with example usage: obj.test_torch_encode_plus_sent_to_model() without return types