code: def forward_model(self, inp):
      sample = self.input_to_batch(inp)
      txt_tokens = sample["txt_tokens"]  # [B, T_t]
      spk_id = sample.get("spk_ids")
  
      output = self.model(
          txt_tokens,
          spk_id=spk_id,
          ref_mels=None,
          infer=True,
          pitch_midi=sample["pitch_midi"],
          midi_dur=sample["midi_dur"],
          is_slur=sample["is_slur"],
      )
      mel_out = output["mel_out"]  # [B, T,80]
      mel_out = rt.OrtValue.ortvalue_from_numpy(
          mel_out, self.device_name, 0
      )
      if hparams.get("pe_enable") is not None and hparams["pe_enable"]:
          # pe predict from Pred mel
          io_binding = self.pe.io_binding()
          io_binding.bind_ortvalue_input("mel_input", mel_out)
          io_binding.bind_output("f0_denorm_pred")
          self.pe.run_with_iobinding(io_binding)
          f0_pred = io_binding.get_outputs()[0]
      else:
          f0_pred = output["f0_denorm"]
      wav_out = self.run_vocoder(mel_out, f0=f0_pred.numpy())
  
      return wav_out[0]
  
imports: import os
  from collections import deque
  import librosa
  import numpy
  import onnxruntime
  from pypinyin import lazy_pinyin
  from tqdm import tqdm
  from .inference.svs.opencpop.map import cpop_pinyin2ph_func
  from .utils.hparams import hparams
  from .utils.text_encoder import TokenTextEncoder
label_desc: Use image node_id 5 for calling the Infer obj's underlying member method code with example usage: obj.forward_model(inp) and returns: wav_out