code: def __init__(
      self,
      args: "ModelArgs",
      tokenizer: "TTSTokenizer" = None,
      speaker_manager: "SpeakerManager" = None,
  ):
      super().__init__()
      self.args = args
      self.tokenizer = tokenizer
      self.speaker_manager = speaker_manager
  
      self.init_multispeaker(args)
      # self.set_embedding_dims()
  
      self.length_scale = (
          float(self.args.length_scale)
          if isinstance(self.args.length_scale, int)
          else self.args.length_scale
      )
  
      self.emb_dim = args.n_hidden_conformer_encoder
      self.encoder = Conformer(
          dim=self.args.n_hidden_conformer_encoder,
          n_layers=self.args.n_layers_conformer_encoder,
          n_heads=self.args.n_heads_conformer_encoder,
          speaker_embedding_dim=self.embedded_speaker_dim,
          p_dropout=self.args.dropout_conformer_encoder,
          kernel_size_conv_mod=self.args.kernel_size_conv_mod_conformer_encoder,
          lrelu_slope=self.args.lrelu_slope,
      )
      self.pitch_adaptor = PitchAdaptor(
          n_input=self.args.n_hidden_conformer_encoder,
          n_hidden=self.args.n_hidden_variance_adaptor,
          n_out=1,
          kernel_size=self.args.kernel_size_variance_adaptor,
          emb_kernel_size=self.args.emb_kernel_size_variance_adaptor,
          p_dropout=self.args.dropout_variance_adaptor,
          lrelu_slope=self.args.lrelu_slope,
      )
      self.energy_adaptor = EnergyAdaptor(
          channels_in=self.args.n_hidden_conformer_encoder,
          channels_hidden=self.args.n_hidden_variance_adaptor,
          channels_out=1,
          kernel_size=self.args.kernel_size_variance_adaptor,
          emb_kernel_size=self.args.emb_kernel_size_variance_adaptor,
          dropout=self.args.dropout_variance_adaptor,
          lrelu_slope=self.args.lrelu_slope,
      )
  
      self.aligner = AlignmentNetwork(
          in_query_channels=self.args.out_channels,
          in_key_channels=self.args.n_hidden_conformer_encoder,
      )
  
      self.duration_predictor = VariancePredictor(
          channels_in=self.args.n_hidden_conformer_encoder,
          channels=self.args.n_hidden_variance_adaptor,
          channels_out=1,
          kernel_size=self.args.kernel_size_variance_adaptor,
          p_dropout=self.args.dropout_variance_adaptor,
          lrelu_slope=self.args.lrelu_slope,
      )
  
      self.utterance_prosody_encoder = UtteranceLevelProsodyEncoder(
          num_mels=self.args.num_mels,
          ref_enc_filters=self.args.ref_enc_filters_reference_encoder,
          ref_enc_size=self.args.ref_enc_size_reference_encoder,
          ref_enc_gru_size=self.args.ref_enc_gru_size_reference_encoder,
          ref_enc_strides=self.args.ref_enc_strides_reference_encoder,
          n_hidden=self.args.n_hidden_conformer_encoder,
          dropout=self.args.dropout_conformer_encoder,
          bottleneck_size_u=self.args.bottleneck_size_u_reference_encoder,
          token_num=self.args.token_num_reference_encoder,
      )
  
      self.utterance_prosody_predictor = PhonemeProsodyPredictor(
          hidden_size=self.args.n_hidden_conformer_encoder,
          kernel_size=self.args.predictor_kernel_size_reference_encoder,
          dropout=self.args.dropout_conformer_encoder,
          bottleneck_size=self.args.bottleneck_size_u_reference_encoder,
          lrelu_slope=self.args.lrelu_slope,
      )
  
      self.phoneme_prosody_encoder = PhonemeLevelProsodyEncoder(
          num_mels=self.args.num_mels,
          ref_enc_filters=self.args.ref_enc_filters_reference_encoder,
          ref_enc_size=self.args.ref_enc_size_reference_encoder,
          ref_enc_gru_size=self.args.ref_enc_gru_size_reference_encoder,
          ref_enc_strides=self.args.ref_enc_strides_reference_encoder,
          n_hidden=self.args.n_hidden_conformer_encoder,
          dropout=self.args.dropout_conformer_encoder,
          bottleneck_size_p=self.args.bottleneck_size_p_reference_encoder,
          n_heads=self.args.n_heads_conformer_encoder,
      )
  
      self.phoneme_prosody_predictor = PhonemeProsodyPredictor(
          hidden_size=self.args.n_hidden_conformer_encoder,
          kernel_size=self.args.predictor_kernel_size_reference_encoder,
          dropout=self.args.dropout_conformer_encoder,
          bottleneck_size=self.args.bottleneck_size_p_reference_encoder,
          lrelu_slope=self.args.lrelu_slope,
      )
  
      self.u_bottle_out = nn.Linear(
          self.args.bottleneck_size_u_reference_encoder,
          self.args.n_hidden_conformer_encoder,
      )
  
      self.u_norm = nn.InstanceNorm1d(
          self.args.bottleneck_size_u_reference_encoder
      )
      self.p_bottle_out = nn.Linear(
          self.args.bottleneck_size_p_reference_encoder,
          self.args.n_hidden_conformer_encoder,
      )
      self.p_norm = nn.InstanceNorm1d(
          self.args.bottleneck_size_p_reference_encoder,
      )
      self.decoder = Conformer(
          dim=self.args.n_hidden_conformer_decoder,
          n_layers=self.args.n_layers_conformer_decoder,
          n_heads=self.args.n_heads_conformer_decoder,
          speaker_embedding_dim=self.embedded_speaker_dim,
          p_dropout=self.args.dropout_conformer_decoder,
          kernel_size_conv_mod=self.args.kernel_size_conv_mod_conformer_decoder,
          lrelu_slope=self.args.lrelu_slope,
      )
  
      padding_idx = self.tokenizer.characters.pad_id
      self.src_word_emb = EmbeddingPadded(
          self.args.num_chars,
          self.args.n_hidden_conformer_encoder,
          padding_idx=padding_idx,
      )
      self.to_mel = nn.Linear(
          self.args.n_hidden_conformer_decoder,
          self.args.num_mels,
      )
  
      self.energy_scaler = torch.nn.BatchNorm1d(
          1, affine=False, track_running_stats=True, momentum=None
      )
      self.energy_scaler.requires_grad_(False)
  
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 1 to create a new AcousticModel object from inherited base classes: torch.nn with example: obj = AcousticModel(args, tokenizer, speaker_manager)