code: def test_enh_diar_model(
      enh_encoder,
      enh_decoder,
      enh_separator,
      mask_module,
      training,
      loss_wrappers,
      diar_frontend,
      diar_encoder,
      diar_decoder,
      label_aggregator,
  ):
      inputs = torch.randn(2, 300)
      speech_ref = torch.randn(2, 300).float()
      text = torch.randint(high=2, size=(2, 300, 2))
      enh_model = ESPnetEnhancementModel(
          encoder=enh_encoder,
          separator=enh_separator,
          decoder=enh_decoder,
          mask_module=mask_module,
          loss_wrappers=loss_wrappers,
      )
      diar_model = ESPnetDiarizationModel(
          label_aggregator=label_aggregator,
          frontend=diar_frontend,
          encoder=diar_encoder,
          decoder=diar_decoder,
          specaug=None,
          normalize=None,
          attractor=None,
      )
      enh_s2t_model = ESPnetEnhS2TModel(
          enh_model=enh_model,
          s2t_model=diar_model,
      )
  
      if training:
          enh_s2t_model.train()
      else:
          enh_s2t_model.eval()
  
      kwargs = {
          "speech": inputs,
          "speech_ref1": speech_ref,
          "speech_ref2": speech_ref,
          "text": text,
      }
      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 3 for calling a global function with example usage: test_enh_diar_model(enh_encoder, enh_decoder, enh_separator, mask_module, training, loss_wrappers, diar_frontend, diar_encoder, diar_decoder, label_aggregator) without return types