code: def test_long_sequence_splitting_end_to_end(self):
      # Mostly the same as the end_to_end test (except for adding max_length=4),
      # because we don't want this splitting behavior to change input/output format.
  
      tokenizer = PretrainedTransformerTokenizer(
          model_name="bert-base-uncased"
      )
      token_indexer = PretrainedTransformerIndexer(
          model_name="bert-base-uncased", max_length=4
      )
  
      sentence1 = "A, AllenNLP sentence."
      tokens1 = tokenizer.tokenize(sentence1)
      sentence2 = "AllenNLP is great"
      tokens2 = tokenizer.tokenize(sentence2)
  
      vocab = Vocabulary()
  
      params = Params(
          {
              "token_embedders": {
                  "bert": {
                      "type": "pretrained_transformer",
                      "model_name": "bert-base-uncased",
                      "max_length": 4,
                  }
              }
          }
      )
      token_embedder = BasicTextFieldEmbedder.from_params(
          vocab=vocab, params=params
      )
  
      instance1 = Instance(
          {"tokens": TextField(tokens1, {"bert": token_indexer})}
      )
      instance2 = Instance(
          {"tokens": TextField(tokens2, {"bert": token_indexer})}
      )
  
      batch = Batch([instance1, instance2])
      batch.index_instances(vocab)
  
      padding_lengths = batch.get_padding_lengths()
      tensor_dict = batch.as_tensor_dict(padding_lengths)
      tokens = tensor_dict["tokens"]
      max_length = max(len(tokens1), len(tokens2))
  
      # Adds n_segments * 2 special tokens
      segment_concat_length = (
          int(math.ceil(max_length / 4)) * 2 + max_length
      )
      assert tokens["bert"]["token_ids"].shape == (
          2,
          segment_concat_length,
      )
  
      assert tokens["bert"]["mask"].tolist() == [
          [True, True, True, True, True, True, True, True, True],
          [True, True, True, True, True, True, True, False, False],
      ]
      assert tokens["bert"]["segment_concat_mask"].tolist() == [
          [True] * segment_concat_length,
          [True] * (segment_concat_length - 4)
          + [False] * 4,  # 4 is hard-coded length difference
      ]
  
      # Attention mask
      bert_vectors = token_embedder(tokens)
      assert bert_vectors.size() == (2, 9, 768)
  
imports: import math
  import pytest
  import torch
  from allennlp.common import Params, cached_transformers
  from allennlp.common.testing import AllenNlpTestCase, requires_gpu
  from allennlp.data import Vocabulary
  from allennlp.data.batch import Batch
  from allennlp.data.fields import TextField
  from allennlp.data.instance import Instance
  from allennlp.data.token_indexers import PretrainedTransformerIndexer
  from allennlp.data.tokenizers import PretrainedTransformerTokenizer
  from allennlp.modules.text_field_embedders import BasicTextFieldEmbedder
  from allennlp.modules.token_embedders import PretrainedTransformerEmbedder
label_desc: Use image node_id 7 for calling the TestPretrainedTransformerEmbedder obj's underlying member method code with example usage: obj.test_long_sequence_splitting_end_to_end() without return types