code: def test_enh_asr_model(
      enh_encoder,
      enh_decoder,
      enh_separator,
      training,
      loss_wrappers,
      frontend,
      s2t_encoder,
      s2t_decoder,
      s2t_ctc,
  ):
      inputs = torch.randn(2, 300)
      ilens = torch.LongTensor([300, 200])
      speech_ref = torch.randn(2, 300).float()
      text = torch.LongTensor([[1, 2, 3, 4, 5], [5, 4, 3, 2, 1]])
      text_lengths = torch.LongTensor([5, 5])
      enh_model = ESPnetEnhancementModel(
          encoder=enh_encoder,
          separator=enh_separator,
          decoder=enh_decoder,
          mask_module=None,
          loss_wrappers=loss_wrappers,
      )
      s2t_model = ESPnetASRModel(
          vocab_size=len(token_list),
          token_list=token_list,
          frontend=frontend,
          encoder=s2t_encoder,
          decoder=s2t_decoder,
          ctc=s2t_ctc,
          specaug=None,
          normalize=None,
          preencoder=None,
          postencoder=None,
          joint_network=None,
      )
      enh_s2t_model = ESPnetEnhS2TModel(
          enh_model=enh_model,
          s2t_model=s2t_model,
      )
  
      if training:
          enh_s2t_model.train()
      else:
          enh_s2t_model.eval()
  
      kwargs = {
          "speech": inputs,
          "speech_lengths": ilens,
          "speech_ref1": speech_ref,
          "text_spk1": text,
          "text_spk1_lengths": text_lengths,
      }
      loss, stats, weight = enh_s2t_model(**kwargs)
  
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.asr.espnet_model import ESPnetASRModel
  from espnet2.asr.frontend.default import DefaultFrontend
  from espnet2.diar.decoder.linear_decoder import LinearDecoder
  from espnet2.diar.espnet_model import ESPnetDiarizationModel
  from espnet2.diar.layers.multi_mask import MultiMask
  from espnet2.diar.separator.tcn_separator_nomask import TCNSeparatorNomask
  from espnet2.enh.decoder.conv_decoder import ConvDecoder
  from espnet2.enh.decoder.stft_decoder import STFTDecoder
  from espnet2.enh.encoder.conv_encoder import ConvEncoder
  from espnet2.enh.encoder.stft_encoder import STFTEncoder
  from espnet2.enh.espnet_enh_s2t_model import ESPnetEnhS2TModel
  from espnet2.enh.espnet_model import ESPnetEnhancementModel
  from espnet2.enh.loss.criterions.time_domain import SISNRLoss
  from espnet2.enh.loss.wrappers.fixed_order import FixedOrderSolver
  from espnet2.enh.loss.wrappers.pit_solver import PITSolver
  from espnet2.enh.separator.neural_beamformer import NeuralBeamformer
  from espnet2.enh.separator.rnn_separator import RNNSeparator
  from espnet2.layers.label_aggregation import LabelAggregate
label_desc: Use image node_id 1 for calling a global function with example usage: test_enh_asr_model(enh_encoder, enh_decoder, enh_separator, training, loss_wrappers, frontend, s2t_encoder, s2t_decoder, s2t_ctc) without return types