code: def test_NaiveRNN(
      eprenet_conv_layers,
      midi_embed_integration_type,
      postnet_layers,
      reduction_factor,
      spk_embed_dim,
      spk_embed_integration_type,
      spks,
      langs,
      loss_type,
  ):
      idim = 10
      odim = 4
      model = NaiveRNN(
          idim=idim,
          odim=odim,
          midi_dim=129,
          embed_dim=5,
          eprenet_conv_layers=eprenet_conv_layers,
          eprenet_conv_chans=4,
          eprenet_conv_filts=5,
          elayers=2,
          eunits=6,
          ebidirectional=True,
          midi_embed_integration_type=midi_embed_integration_type,
          dlayers=2,
          dunits=6,
          postnet_layers=postnet_layers,
          postnet_chans=4,
          postnet_filts=5,
          use_batch_norm=True,
          reduction_factor=reduction_factor,
          spks=spks,
          langs=langs,
          spk_embed_dim=spk_embed_dim,
          spk_embed_integration_type=spk_embed_integration_type,
          eprenet_dropout_rate=0.2,
          edropout_rate=0.1,
          ddropout_rate=0.1,
          postnet_dropout_rate=0.5,
          init_type="pytorch",
          use_masking=True,
          use_weighted_masking=False,
          loss_type=loss_type,
      )
  
      inputs = dict(
          text=torch.randint(0, idim, (2, 8)),
          text_lengths=torch.tensor([8, 5], dtype=torch.long),
          feats=torch.randn(2, 16 * reduction_factor, odim),
          feats_lengths=torch.tensor([16, 10], dtype=torch.long)
          * reduction_factor,
          label={
              "lab": torch.randint(0, idim, (2, 16)),
              "score": torch.randint(0, idim, (2, 16)),
          },
          label_lengths={
              "lab": torch.tensor([16, 10], dtype=torch.long),
              "score": torch.tensor([16, 12], dtype=torch.long),
          },
          melody={
              "lab": torch.randint(0, 127, (2, 16)),
              "score": torch.randint(0, 127, (2, 16)),
          },
          melody_lengths={
              "lab": torch.tensor([16, 10], dtype=torch.long),
              "score": torch.tensor([16, 12], dtype=torch.long),
          },
          duration={
              "lab": torch.tensor(
                  [
                      [1, 2, 2, 2, 2, 3, 3, 3, 1, 3, 3, 3, 2, 2, 2, 2],
                      [2, 2, 2, 2, 1, 3, 3, 3, 1, 1, 0, 0, 0, 0, 0, 0],
                  ],
                  dtype=torch.int64,
              ),
              "score_phn": torch.tensor(
                  [
                      [1, 2, 2, 2, 2, 3, 3, 3, 1, 3, 3, 3, 2, 2, 2, 2],
                      [2, 2, 2, 2, 1, 4, 4, 4, 4, 1, 2, 2, 0, 0, 0, 0],
                  ],
                  dtype=torch.int64,
              ),
          },
          duration_lengths={
              "lab": torch.tensor([16, 10], dtype=torch.long),
              "score_phn": torch.tensor([16, 12], dtype=torch.long),
          },
          slur=torch.randint(0, 2, (2, 8)),
          slur_lengths=torch.tensor([8, 5], dtype=torch.long),
          pitch=torch.randn(2, 16 * reduction_factor, 1),
          pitch_lengths=torch.tensor([16, 10], dtype=torch.long)
          * reduction_factor,
      )
      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,
                  idim,
                  (
                      1,
                      5,
                  ),
              ),
              label={
                  "lab": torch.randint(
                      0,
                      idim,
                      (
                          1,
                          11,
                      ),
                  ),
                  "score": torch.randint(
                      0,
                      idim,
                      (
                          1,
                          12,
                      ),
                  ),
              },
              melody={
                  "lab": torch.randint(
                      0,
                      127,
                      (
                          1,
                          11,
                      ),
                  ),
                  "score": torch.randint(
                      0,
                      127,
                      (
                          1,
                          12,
                      ),
                  ),
              },
              duration={
                  "lab": torch.tensor(
                      [[1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3]],
                      dtype=torch.int64,
                  ),
                  "score_phn": torch.tensor(
                      [[1, 2, 2, 2, 2, 3, 3, 3, 4, 4, 4, 4]],
                      dtype=torch.int64,
                  ),
              },
              slur=torch.randint(0, 2, (1, 5)),
              pitch=torch.randn(11 * reduction_factor, 1),
          )
          if spks > 0:
              inputs["sids"] = torch.randint(0, spks, (1,))
          if langs > 0:
              inputs["lids"] = torch.randint(0, langs, (1,))
          if spk_embed_dim is not None:
              inputs.update(spembs=torch.randn(spk_embed_dim))
          model.inference(**inputs)
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_NaiveRNN(eprenet_conv_layers, midi_embed_integration_type, postnet_layers, reduction_factor, spk_embed_dim, spk_embed_integration_type, spks, langs, loss_type) without return types