code: def test_neural_beamformer_forward_backward(
      n_fft,
      win_length,
      hop_length,
      num_spk,
      loss_type,
      use_wpe,
      wnet_type,
      wlayers,
      wunits,
      wprojs,
      taps,
      delay,
      use_dnn_mask_for_wpe,
      multi_source_wpe,
      use_beamformer,
      bnet_type,
      blayers,
      bunits,
      bprojs,
      badim,
      ref_channel,
      use_noise_mask,
      bnonlinear,
      beamformer_type,
  ):
      # Skip some cases
      if num_spk > 1 and use_wpe and use_beamformer:
          if not multi_source_wpe:
              # Single-source WPE is not supported with beamformer in multi-speaker cases
              return
      elif num_spk == 1:
          if multi_source_wpe:
              # When num_spk == 1, `multi_source_wpe` has no effect
              return
          elif beamformer_type in (
              "lcmv",
              "lcmp",
              "wlcmp",
              "mvdr_tfs",
              "mvdr_tfs_souden",
          ):
              # only support multiple-source cases
              return
      if bnonlinear != "sigmoid" and (
          beamformer_type != "mvdr_souden" or multi_source_wpe
      ):
          # only test different nonlinear layers with MVDR_Souden
          return
  
      # ensures reproducibility and reversibility in the matrix inverse computation
      torch.random.manual_seed(0)
      stft = STFTEncoder(
          n_fft=n_fft,
          win_length=win_length,
          hop_length=hop_length,
          use_builtin_complex=is_torch_1_12_1_plus,
      )
      model = NeuralBeamformer(
          stft.output_dim,
          num_spk=num_spk,
          loss_type=loss_type,
          use_wpe=use_wpe,
          wnet_type=wnet_type,
          wlayers=wlayers,
          wunits=wunits,
          wprojs=wprojs,
          taps=taps,
          delay=delay,
          use_dnn_mask_for_wpe=use_dnn_mask_for_wpe,
          use_beamformer=use_beamformer,
          bnet_type=bnet_type,
          blayers=blayers,
          bunits=bunits,
          bprojs=bprojs,
          badim=badim,
          ref_channel=ref_channel,
          use_noise_mask=use_noise_mask,
          beamformer_type=beamformer_type,
          rtf_iterations=2,
          shared_power=True,
          use_torchaudio_api=is_torch_1_12_1_plus,
      )
  
      model.train()
      inputs = random_speech[..., :2].float()
      ilens = torch.LongTensor([16, 12])
      input_spectrum, flens = stft(inputs, ilens)
      est_speech, flens, others = model(input_spectrum, flens)
      if loss_type.startswith("mask"):
          assert est_speech is None
          loss = sum([abs(m).mean() for m in others.values()])
      else:
          loss = sum([abs(est).mean() for est in est_speech])
      loss.backward()
  
file_path: espnet/test/espnet2/enh/separator/test_beamformer.py
imports: import numpy
  import pytest
  import torch
  from packaging.version import parse
  from espnet2.enh.encoder.stft_encoder import STFTEncoder
  from espnet2.enh.layers.complex_utils import is_torch_complex_tensor
  from espnet2.enh.layers.dnn_beamformer import BEAMFORMER_TYPES
  from espnet2.enh.separator.neural_beamformer import NeuralBeamformer
label_desc: Use image node_id 1 for calling a global function with example usage: test_neural_beamformer_forward_backward(n_fft, win_length, hop_length, num_spk, loss_type, use_wpe, wnet_type, wlayers, wunits, wprojs, taps, delay, use_dnn_mask_for_wpe, multi_source_wpe, use_beamformer, bnet_type, blayers, bunits, bprojs, badim, ref_channel, use_noise_mask, bnonlinear, beamformer_type) without return types