code: def test_transformers_forward(
      model_name_or_path, length_adaptor_n_layers, lang_token_id
  ):
      if not is_torch_1_8_plus:
          return
      idim = 400
      postencoder = HuggingFaceTransformersPostEncoder(
          idim,
          model_name_or_path,
          length_adaptor_n_layers,
          lang_token_id,
      )
      x = torch.randn([4, 50, idim], requires_grad=True)
      x_lengths = torch.LongTensor([20, 5, 50, 15])
      y, y_lengths = postencoder(x, x_lengths)
      y.sum().backward()
      odim = postencoder.output_size()
  
      y_shape_1_expected = 50 // 2**length_adaptor_n_layers
      y_lengths_expected = (
          x_lengths.float()
          .div(2**length_adaptor_n_layers)
          .floor()
          .long()
      )
  
      if lang_token_id != -1:
          y_shape_1_expected += 1
          y_lengths_expected += 1
  
      assert y.shape == torch.Size([4, y_shape_1_expected, odim])
      assert torch.equal(y_lengths, y_lengths_expected)
  
imports: import pytest
  import torch
  from packaging.version import parse
  from espnet2.asr.postencoder.hugging_face_transformers_postencoder import HuggingFaceTransformersPostEncoder
label_desc: Use image node_id 1 for calling a global function with example usage: test_transformers_forward(model_name_or_path, length_adaptor_n_layers, lang_token_id) without return types