code: def test_espnet_model(encoder_arch, decoder_arch):
      token_list = [
          "<blank>",
          "<unk>",
          "<na>",
          "<nospeech>",
          "<en>",
          "<asr>",
          "<st_en>" "<notimestamps>",
          "<0.00>",
          "<30.00>",
          "a",
          "i",
          "<sos>",
          "<eos>",
          "<sop>",
      ]
      vocab_size = len(token_list)
      enc_out = 4
      encoder = encoder_arch(
          20, output_size=enc_out, linear_units=4, num_blocks=2
      )
      decoder = decoder_arch(
          vocab_size, enc_out, linear_units=4, num_blocks=2
      )
      ctc = CTC(odim=vocab_size, encoder_output_size=enc_out)
  
      model = ESPnetS2TModel(
          vocab_size,
          token_list=token_list,
          frontend=None,
          specaug=None,
          normalize=None,
          preencoder=None,
          encoder=encoder,
          postencoder=None,
          decoder=decoder,
          ctc=ctc,
      )
  
      inputs = dict(
          speech=torch.randn(2, 10, 20, requires_grad=True),
          speech_lengths=torch.tensor([10, 8], dtype=torch.long),
          text=torch.randint(2, 4, [2, 4], dtype=torch.long),
          text_lengths=torch.tensor([4, 3], dtype=torch.long),
          text_prev=torch.randint(2, 4, [2, 4], dtype=torch.long),
          text_prev_lengths=torch.tensor([4, 3], dtype=torch.long),
          text_ctc=torch.randint(2, 4, [2, 4], dtype=torch.long),
          text_ctc_lengths=torch.tensor([4, 3], dtype=torch.long),
      )
      loss, *_ = model(**inputs)
      loss.backward()
  
imports: import pytest
  import torch
  from espnet2.asr.ctc import CTC
  from espnet2.asr.decoder.transformer_decoder import TransformerDecoder
  from espnet2.asr.encoder.transformer_encoder import TransformerEncoder
  from espnet2.s2t.espnet_model import ESPnetS2TModel
label_desc: Use image node_id 1 for calling a global function with example usage: test_espnet_model(encoder_arch, decoder_arch) without return types