code: def test_uses_forward(
      training, n_mics, num_spk, use_builtin_complex, loss_wrappers
  ):
      if n_mics == 1:
          inputs = torch.randn(1, 300)
      else:
          inputs = torch.randn(1, 300, n_mics)
      ilens = torch.LongTensor([300])
      speech_refs = [
          torch.randn(1, 300).float(),
          torch.randn(1, 300).float(),
      ]
  
      encoder = STFTEncoder(
          n_fft=8, hop_length=2, use_builtin_complex=use_builtin_complex
      )
      decoder = STFTDecoder(n_fft=8, hop_length=2)
  
      separator = USESSeparator(
          input_dim=None,
          num_spk=num_spk,
          enc_channels=12,
          bottleneck_size=8,
          num_blocks=3,
          num_spatial_blocks=2,
          ref_channel=0,
          segment_size=64,
          memory_size=2,
          # Transformer-related arguments
          hidden_size=4,
          att_heads=2,
          ch_mode="tac",
          ch_att_dim=8,
      )
      enh_model = ESPnetEnhancementModel(
          encoder=encoder,
          separator=separator,
          decoder=decoder,
          mask_module=None,
          loss_wrappers=loss_wrappers,
      )
  
      if training:
          enh_model.train()
      else:
          enh_model.eval()
  
      kwargs = {
          "speech_mix": inputs,
          "speech_mix_lengths": ilens,
          **{
              "speech_ref{}".format(i + 1): speech_refs[i]
              for i in range(num_spk)
          },
      }
      loss, stats, weight = enh_model(**kwargs)
  
imports: import pytest
  import torch
  from packaging.version import parse
  from espnet2.enh.decoder.conv_decoder import ConvDecoder
  from espnet2.enh.decoder.null_decoder import NullDecoder
  from espnet2.enh.decoder.stft_decoder import STFTDecoder
  from espnet2.enh.encoder.conv_encoder import ConvEncoder
  from espnet2.enh.encoder.null_encoder import NullEncoder
  from espnet2.enh.encoder.stft_encoder import STFTEncoder
  from espnet2.enh.espnet_model import ESPnetEnhancementModel
  from espnet2.enh.loss.criterions.tf_domain import FrequencyDomainL1, FrequencyDomainMSE
  from espnet2.enh.loss.criterions.time_domain import SISNRLoss
  from espnet2.enh.loss.wrappers.fixed_order import FixedOrderSolver
  from espnet2.enh.loss.wrappers.multilayer_pit_solver import MultiLayerPITSolver
  from espnet2.enh.loss.wrappers.pit_solver import PITSolver
  from espnet2.enh.separator.conformer_separator import ConformerSeparator
  from espnet2.enh.separator.dc_crn_separator import DC_CRNSeparator
  from espnet2.enh.separator.dccrn_separator import DCCRNSeparator
  from espnet2.enh.separator.dprnn_separator import DPRNNSeparator
  from espnet2.enh.separator.dptnet_separator import DPTNetSeparator
  from espnet2.enh.separator.ineube_separator import iNeuBe
  from espnet2.enh.separator.neural_beamformer import NeuralBeamformer
  from espnet2.enh.separator.rnn_separator import RNNSeparator
  from espnet2.enh.separator.svoice_separator import SVoiceSeparator
  from espnet2.enh.separator.tcn_separator import TCNSeparator
  from espnet2.enh.separator.tfgridnet_separator import TFGridNet
  from espnet2.enh.separator.tfgridnetv2_separator import TFGridNetV2
  from espnet2.enh.separator.transformer_separator import TransformerSeparator
  from espnet2.enh.separator.uses_separator import USESSeparator
label_desc: Use image node_id 10 for calling a global function with example usage: test_uses_forward(training, n_mics, num_spk, use_builtin_complex, loss_wrappers) without return types