code: def inference(
      self,
      tokens: torch.Tensor,
      speaker_idx: torch.Tensor,
      p_control: float = None,  # TODO # pylint: disable=unused-argument
      d_control: float = None,  # TODO # pylint: disable=unused-argument
      d_vectors: torch.Tensor = None,
      pitch_transform: Callable = None,
      energy_transform: Callable = None,
  ) -> torch.Tensor:
      src_mask = get_mask_from_lengths(
          torch.tensor(
              [tokens.shape[1]], dtype=torch.int64, device=tokens.device
          )
      )
      src_lens = torch.tensor(tokens.shape[1:2]).to(
          tokens.device
      )  # pylint: disable=unused-variable
      (
          sid,
          g,
          lid,
          _,
      ) = self._set_cond_input(  # pylint: disable=unused-variable
          {"d_vectors": d_vectors, "speaker_ids": speaker_idx}
      )  # pylint: disable=unused-variable
  
      token_embeddings = self.src_word_emb(tokens)
      token_embeddings = token_embeddings.masked_fill(
          src_mask.unsqueeze(-1), 0.0
      )
  
      # Embeddings
      speaker_embedding = None
      if d_vectors is not None:
          speaker_embedding = g
      elif speaker_idx is not None:
          speaker_embedding = F.normalize(self.emb_g(sid))
  
      pos_encoding = positional_encoding(
          self.emb_dim,
          token_embeddings.shape[1],
          device=token_embeddings.device,
      )
      encoder_outputs = self.encoder(
          token_embeddings,
          src_mask,
          speaker_embedding=speaker_embedding,
          encoding=pos_encoding,
      )
  
      u_prosody_pred = self.u_norm(
          self.average_utterance_prosody(
              u_prosody_pred=self.utterance_prosody_predictor(
                  x=encoder_outputs, mask=src_mask
              ),
              src_mask=src_mask,
          )
      )
      encoder_outputs = encoder_outputs + self.u_bottle_out(
          u_prosody_pred
      ).expand_as(encoder_outputs)
  
      p_prosody_pred = self.p_norm(
          self.phoneme_prosody_predictor(
              x=encoder_outputs,
              mask=src_mask,
          )
      )
      encoder_outputs = encoder_outputs + self.p_bottle_out(
          p_prosody_pred
      ).expand_as(encoder_outputs)
  
      encoder_outputs_res = encoder_outputs
  
      (
          pitch_emb_pred,
          pitch_pred,
      ) = self.pitch_adaptor.get_pitch_embedding(
          x=encoder_outputs,
          mask=src_mask,
          pitch_transform=pitch_transform,
          pitch_mean=self.pitch_mean
          if hasattr(self, "pitch_mean")
          else None,
          pitch_std=self.pitch_std
          if hasattr(self, "pitch_std")
          else None,
      )
  
      (
          energy_emb_pred,
          energy_pred,
      ) = self.energy_adaptor.get_energy_embedding(
          x=encoder_outputs,
          mask=src_mask,
          energy_transform=energy_transform,
      )
      encoder_outputs = (
          encoder_outputs.transpose(1, 2)
          + pitch_emb_pred
          + energy_emb_pred
      )
  
      log_duration_pred = self.duration_predictor(
          x=encoder_outputs_res.detach(), mask=src_mask
      )  # [B, C_hidden, T_src] -> [B, T_src]
      duration_pred = (
          (torch.exp(log_duration_pred) - 1)
          * (~src_mask)
          * self.length_scale
      )  # -> [B, T_src]
      duration_pred[duration_pred < 1] = 1.0  # -> [B, T_src]
      duration_pred = torch.round(duration_pred)  # -> [B, T_src]
      mel_lens = duration_pred.sum(1)  # -> [B,]
  
      (
          _,
          encoder_outputs_ex,
          alignments,
      ) = self._expand_encoder_with_durations(
          o_en=encoder_outputs,
          y_lengths=mel_lens,
          dr=duration_pred.squeeze(1),
          x_mask=~src_mask[:, None],
      )
  
      mel_mask = get_mask_from_lengths(
          torch.tensor(
              [encoder_outputs_ex.shape[2]],
              dtype=torch.int64,
              device=encoder_outputs_ex.device,
          )
      )
  
      if encoder_outputs_ex.shape[1] > pos_encoding.shape[1]:
          encoding = positional_encoding(
              self.emb_dim,
              encoder_outputs_ex.shape[2],
              device=tokens.device,
          )
  
      # [B, C_hidden, T_src], [B, 1, T_src], [B, C_emb], [B, T_src, C_hidden] -> [B, C_hidden, T_src]
      x = self.decoder(
          encoder_outputs_ex.transpose(1, 2),
          mel_mask,
          speaker_embedding=speaker_embedding,
          encoding=encoding,
      )
      x = self.to_mel(x)
      outputs = {
          "model_outputs": x,
          "alignments": alignments,
          # "pitch": pitch_emb_pred,
          "durations": duration_pred,
          "pitch": pitch_pred,
          "energy": energy_pred,
          "spk_emb": speaker_embedding,
      }
      return outputs
  
imports: from typing import Callable, Dict, Tuple
  import torch
  import torch.nn.functional
  from coqpit import Coqpit
  from torch import nn
  from TTS.tts.layers.delightful_tts.conformer import Conformer
  from TTS.tts.layers.delightful_tts.encoders import PhonemeLevelProsodyEncoder, UtteranceLevelProsodyEncoder, get_mask_from_lengths
  from TTS.tts.layers.delightful_tts.energy_adaptor import EnergyAdaptor
  from TTS.tts.layers.delightful_tts.networks import EmbeddingPadded, positional_encoding
  from TTS.tts.layers.delightful_tts.phoneme_prosody_predictor import PhonemeProsodyPredictor
  from TTS.tts.layers.delightful_tts.pitch_adaptor import PitchAdaptor
  from TTS.tts.layers.delightful_tts.variance_predictor import VariancePredictor
  from TTS.tts.layers.generic.aligner import AlignmentNetwork
  from TTS.tts.utils.helpers import generate_path, maximum_path, sequence_mask
label_desc: Use image node_id 13 for calling the AcousticModel obj's underlying member method code with example usage: obj.inference(tokens, speaker_idx, p_control, d_control, d_vectors, pitch_transform, energy_transform) and returns: outputs