code: def forward(
      self,
      tokens: torch.Tensor,
      src_lens: torch.Tensor,
      mels: torch.Tensor,
      mel_lens: torch.Tensor,
      pitches: torch.Tensor,
      energies: torch.Tensor,
      attn_priors: torch.Tensor,
      use_ground_truth: bool = True,
      d_vectors: torch.Tensor = None,
      speaker_idx: torch.Tensor = None,
  ) -> Dict[str, torch.Tensor]:
      (
          sid,
          g,
          lid,
          _,
      ) = self._set_cond_input(  # pylint: disable=unused-variable
          {"d_vectors": d_vectors, "speaker_ids": speaker_idx}
      )  # pylint: disable=unused-variable
  
      src_mask = get_mask_from_lengths(src_lens)  # [B, T_src]
      mel_mask = get_mask_from_lengths(mel_lens)  # [B, T_mel]
  
      # Token embeddings
      token_embeddings = self.src_word_emb(
          tokens
      )  # [B, T_src, C_hidden]
      token_embeddings = token_embeddings.masked_fill(
          src_mask.unsqueeze(-1), 0.0
      )
  
      # Alignment network and durations
      (
          aligner_durations,
          aligner_soft,
          aligner_logprob,
          aligner_mas,
      ) = self._forward_aligner(
          x=token_embeddings,
          y=mels.transpose(1, 2),
          x_mask=~src_mask[:, None],
          y_mask=~mel_mask[:, None],
          attn_priors=attn_priors,
      )
      dr = aligner_durations  # [B, T_en]
  
      # 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,
          max(token_embeddings.shape[1], max(mel_lens)),
          device=token_embeddings.device,
      )
      encoder_outputs = self.encoder(
          token_embeddings,
          src_mask,
          speaker_embedding=speaker_embedding,
          encoding=pos_encoding,
      )
  
      u_prosody_ref = self.u_norm(
          self.utterance_prosody_encoder(mels=mels, mel_lens=mel_lens)
      )
      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,
          )
      )
  
      if use_ground_truth:
          encoder_outputs = encoder_outputs + self.u_bottle_out(
              u_prosody_ref
          )
      else:
          encoder_outputs = encoder_outputs + self.u_bottle_out(
              u_prosody_pred
          )
  
      p_prosody_ref = self.p_norm(
          self.phoneme_prosody_encoder(
              x=encoder_outputs,
              src_mask=src_mask,
              mels=mels,
              mel_lens=mel_lens,
              encoding=pos_encoding,
          )
      )
      p_prosody_pred = self.p_norm(
          self.phoneme_prosody_predictor(
              x=encoder_outputs, mask=src_mask
          )
      )
  
      if use_ground_truth:
          encoder_outputs = encoder_outputs + self.p_bottle_out(
              p_prosody_ref
          )
      else:
          encoder_outputs = encoder_outputs + self.p_bottle_out(
              p_prosody_pred
          )
  
      encoder_outputs_res = encoder_outputs
  
      (
          pitch_pred,
          avg_pitch_target,
          pitch_emb,
      ) = self.pitch_adaptor.get_pitch_embedding_train(
          x=encoder_outputs,
          target=pitches,
          dr=dr,
          mask=src_mask,
      )
  
      (
          energy_pred,
          avg_energy_target,
          energy_emb,
      ) = self.energy_adaptor.get_energy_embedding_train(
          x=encoder_outputs,
          target=energies,
          dr=dr,
          mask=src_mask,
      )
  
      encoder_outputs = (
          encoder_outputs.transpose(1, 2) + pitch_emb + energy_emb
      )
      log_duration_prediction = self.duration_predictor(
          x=encoder_outputs_res.detach(), mask=src_mask
      )
  
      (
          mel_pred_mask,
          encoder_outputs_ex,
          alignments,
      ) = self._expand_encoder_with_durations(
          o_en=encoder_outputs,
          y_lengths=mel_lens,
          dr=dr,
          x_mask=~src_mask[:, None],
      )
  
      x = self.decoder(
          encoder_outputs_ex.transpose(1, 2),
          mel_mask,
          speaker_embedding=speaker_embedding,
          encoding=pos_encoding,
      )
      x = self.to_mel(x)
  
      dr = torch.log(dr + 1)
  
      dr_pred = torch.exp(log_duration_prediction) - 1
      alignments_dp = self.generate_attn(
          dr_pred, src_mask.unsqueeze(1), mel_pred_mask
      )  # [B, T_max, T_max2']
  
      return {
          "model_outputs": x,
          "pitch_pred": pitch_pred,
          "pitch_target": avg_pitch_target,
          "energy_pred": energy_pred,
          "energy_target": avg_energy_target,
          "u_prosody_pred": u_prosody_pred,
          "u_prosody_ref": u_prosody_ref,
          "p_prosody_pred": p_prosody_pred,
          "p_prosody_ref": p_prosody_ref,
          "alignments_dp": alignments_dp,
          "alignments": alignments,  # [B, T_de, T_en]
          "aligner_soft": aligner_soft,
          "aligner_mas": aligner_mas,
          "aligner_durations": aligner_durations,
          "aligner_logprob": aligner_logprob,
          "dr_log_pred": log_duration_prediction.squeeze(1),  # [B, T]
          "dr_log_target": dr.squeeze(1),  # [B, T]
          "spk_emb": speaker_embedding,
      }
  
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 12 for calling the AcousticModel obj's underlying member method code with example usage: obj.forward(tokens, src_lens, mels, mel_lens, pitches, energies, attn_priors, use_ground_truth, d_vectors, speaker_idx) and returns: dict