code: def test_prodiff(
      reduction_factor,
      spk_embed_dim,
      spk_embed_integration_type,
      diffusion_scheduler,
      encoder_type,
      use_gst,
      spks,
      langs,
  ):
      model = ProDiff(
          idim=10,
          odim=5,
          adim=4,
          aheads=2,
          elayers=1,
          eunits=4,
          reduction_factor=reduction_factor,
          encoder_type=encoder_type,
          duration_predictor_layers=2,
          duration_predictor_chans=4,
          duration_predictor_kernel_size=3,
          energy_predictor_layers=2,
          energy_predictor_chans=4,
          energy_predictor_kernel_size=3,
          energy_predictor_dropout=0.5,
          energy_embed_kernel_size=9,
          energy_embed_dropout=0.5,
          pitch_predictor_layers=2,
          pitch_predictor_chans=4,
          pitch_predictor_kernel_size=3,
          pitch_predictor_dropout=0.5,
          pitch_embed_kernel_size=9,
          pitch_embed_dropout=0.5,
          spks=spks,
          langs=langs,
          spk_embed_dim=spk_embed_dim,
          spk_embed_integration_type=spk_embed_integration_type,
          use_gst=use_gst,
          gst_tokens=2,
          gst_heads=4,
          gst_conv_layers=2,
          gst_conv_chans_list=[2, 4],
          gst_conv_kernel_size=3,
          gst_conv_stride=2,
          gst_gru_layers=1,
          gst_gru_units=4,
          use_masking=False,
          use_weighted_masking=True,
          denoiser_layers=1,
          denoiser_channels=4,
          diffusion_steps=1,
          diffusion_scheduler=diffusion_scheduler,
      )
  
      inputs = dict(
          text=torch.randint(1, 10, (2, 2)),
          text_lengths=torch.tensor([2, 1], dtype=torch.long),
          feats=torch.randn(2, 4 * reduction_factor, 5),
          feats_lengths=torch.tensor([4, 2], dtype=torch.long)
          * reduction_factor,
          durations=torch.tensor(
              [[2, 2, 0], [2, 0, 0]], dtype=torch.long
          ),
          pitch=torch.tensor(
              [[2, 2, 0], [2, 0, 0]], dtype=torch.float
          ).unsqueeze(-1),
          energy=torch.tensor(
              [[2, 2, 0], [2, 0, 0]], dtype=torch.float
          ).unsqueeze(-1),
          # NOTE(kan-bayashi): +1 for eos
          durations_lengths=torch.tensor(
              [2 + 1, 1 + 1], dtype=torch.long
          ),
          pitch_lengths=torch.tensor([2 + 1, 1 + 1], dtype=torch.long),
          energy_lengths=torch.tensor([2 + 1, 1 + 1], dtype=torch.long),
      )
      if spk_embed_dim is not None:
          inputs.update(spembs=torch.randn(2, spk_embed_dim))
      if spks > 0:
          inputs.update(sids=torch.randint(0, spks, (2, 1)))
      if langs > 0:
          inputs.update(lids=torch.randint(0, langs, (2, 1)))
      loss, *_ = model(**inputs)
      loss.backward()
  
      with torch.no_grad():
          model.eval()
  
          inputs = dict(
              text=torch.randint(0, 10, (2,)),
              feats=torch.randn(2, 4 * reduction_factor, 5),
              durations=torch.tensor(
                  [[2, 2, 0], [2, 0, 0]], dtype=torch.long
              ),
          )
          if use_gst:
              inputs.update(feats=torch.randn(5, 5))
          if spk_embed_dim is not None:
              inputs.update(spembs=torch.randn(spk_embed_dim))
          if spks > 0:
              inputs.update(sids=torch.randint(0, spks, (1,)))
          if langs > 0:
              inputs.update(lids=torch.randint(0, langs, (1,)))
          model.inference(**inputs)
  
          # teacher forcing
          inputs.update(
              durations=torch.tensor([2, 2, 0], dtype=torch.long)
          )
          inputs.update(
              pitch=torch.tensor(
                  [2, 2, 0], dtype=torch.float
              ).unsqueeze(-1)
          )
          inputs.update(
              energy=torch.tensor(
                  [2, 2, 0], dtype=torch.float
              ).unsqueeze(-1)
          )
          model.inference(**inputs, use_teacher_forcing=True)
  
imports: import pytest
  import torch
  from packaging.version import parse
  from espnet2.tts.prodiff import ProDiff
  from espnet2.tts.prodiff.loss import SSimLoss
label_desc: Use image node_id 1 for calling a global function with example usage: test_prodiff(reduction_factor, spk_embed_dim, spk_embed_integration_type, diffusion_scheduler, encoder_type, use_gst, spks, langs) without return types