code: def test_transformer_text_field_batching():
      batch = Batch(
          [
              Instance(
                  {
                      "text": TransformerTextField(
                          torch.IntTensor([1, 2, 3])
                      )
                  }
              ),
              Instance(
                  {
                      "text": TransformerTextField(
                          torch.IntTensor([2, 3, 4, 5])
                      )
                  }
              ),
              Instance(
                  {"text": TransformerTextField(torch.IntTensor())}
              ),
          ]
      )
      tensors = batch.as_tensor_dict(batch.get_padding_lengths())
      assert tensors["text"]["input_ids"].shape == (3, 4)
      assert tensors["text"]["input_ids"][0, -1] == 0
      assert tensors["text"]["attention_mask"][0, -1] == torch.Tensor(
          [False]
      )
      assert torch.all(tensors["text"]["input_ids"][-1] == 0)
      assert torch.all(
          tensors["text"]["attention_mask"][-1] == torch.tensor([False])
      )
  
file_path: allennlp/tests/data/fields/transformer_text_field_test.py
imports: import torch
  import pytest
  from allennlp.common.cached_transformers import get_tokenizer
  from allennlp.data import Batch, Instance
  from allennlp.data.fields import TransformerTextField
label_desc: Use image node_id 3 for calling a global function with example usage: test_transformer_text_field_batching() without return types