code: def model(self, txt_tokens, **kwargs):
      fs_input_names = [node.name for node in self.fs2.get_inputs()]
      inputs = {"txt_tokens": txt_tokens}
      inputs.update(
          {
              k: v
              for k, v in kwargs.items()
              if isinstance(v, np.ndarray) and k in fs_input_names
          }
      )
  
      io_binding = self.fs2.io_binding()
      for k, v in inputs.items():
          io_binding.bind_cpu_input(k, v)
      io_binding.bind_output("decoder_inp")
      io_binding.bind_output("mel_out")
      if not self.use_pe:
          io_binding.bind_output("f0_denorm")
      self.fs2.run_with_iobinding(io_binding)
      decoder_inp, mel_out = io_binding.get_outputs()[:2]
      self.device_name = mel_out.device_name()
      ret = {"decoder_inp": decoder_inp, "mel_out": mel_out}
      if not self.use_pe:
          ret.update({"f0_denorm": io_binding.get_outputs()[-1]})
      cond = decoder_inp.numpy().transpose([0, 2, 1])
  
      ret["fs2_mel"] = ret["mel_out"]
      fs2_mels = mel_out.numpy()
      t = self.K_step
      fs2_mels = self.norm_spec(fs2_mels)
      fs2_mels = fs2_mels.transpose([0, 2, 1])[:, None, :, :]
  
      io_binding = self.q_sample.io_binding()
      io_binding.bind_cpu_input("x_start", fs2_mels)
      io_binding.bind_cpu_input(
          "noise",
          np.random.randn(*fs2_mels.shape).astype(fs2_mels.dtype),
      )
      io_binding.bind_cpu_input(
          "t", np.asarray([t - 1], dtype=np.int64)
      )
      io_binding.bind_output("x_next")
      self.q_sample.run_with_iobinding(io_binding)
      x = io_binding.get_outputs()[0].numpy()
      if (
          hparams.get("gaussian_start") is not None
          and hparams["gaussian_start"]
      ):
          print("===> gaussion start.")
          shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2])
          x = np.random.randn(*shape).astype(fs2_mels.dtype)
  
      cond = rt.OrtValue.ortvalue_from_numpy(
          cond, mel_out.device_name(), 0
      )
      x = rt.OrtValue.ortvalue_from_numpy(x, mel_out.device_name(), 0)
  
      if hparams.get("pndm_speedup"):
          self.noise_list = deque(maxlen=4)
          iteration_interval = hparams["pndm_speedup"]
          interval = rt.OrtValue.ortvalue_from_numpy(
              np.asarray([iteration_interval], np.int64),
              mel_out.device_name(),
              0,
          )
          for i in tqdm(
              reversed(range(0, t, iteration_interval)),
              desc="sample time step",
              total=t // iteration_interval,
          ):
              io_binding = self.p_sample_plms.io_binding()
              io_binding.bind_ortvalue_input("x", x)
              io_binding.bind_cpu_input(
                  "noise", np.random.randn(*x.shape).astype(x.dtype)
              )
              io_binding.bind_ortvalue_input("cond", cond)
              io_binding.bind_cpu_input(
                  "t", np.asarray([i], dtype=np.int64)
              )  # torch i-1 but here i
              io_binding.bind_ortvalue_input("interval", interval)
              io_binding.bind_output("x_next")
              self.p_sample_plms.run_with_iobinding(io_binding)
              x = io_binding.get_outputs()[0]
      else:
          for i in tqdm(
              reversed(range(0, t)), desc="sample time step", total=t
          ):
              io_binding = self.p_sample.io_binding()
              io_binding.bind_ortvalue_input("x", x)
              io_binding.bind_cpu_input(
                  "noise",
                  np.random.randn(*x.shape()).astype(np.float32),
              )
              io_binding.bind_ortvalue_input("cond", cond)
              io_binding.bind_cpu_input(
                  "t", np.asarray([i], dtype=np.int64)
              )  # torch i-1 but here i
              io_binding.bind_output("x_next")
              self.p_sample.run_with_iobinding(io_binding)
              x = io_binding.get_outputs()[0]
      x = x.numpy()[:, 0].transpose([0, 2, 1])
      mel2ph = kwargs.get("mel2ph", None)
      if mel2ph is not None:  # for singing
          ret["mel_out"] = self.denorm_spec(x) * (
              (mel2ph > 0).astype(np.float32)[:, :, None]
          )
      else:
          ret["mel_out"] = self.denorm_spec(x)
      return ret
  
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 2 for calling the Infer obj's underlying member method code with example usage: obj.model(txt_tokens) and returns: ret