code: def __init__(self, cfg: DictConfig, trainer: Trainer = None):
      # Convert to Hydra 1.0 compatible DictConfig
      cfg = model_utils.convert_model_config_to_dict_config(cfg)
      cfg = model_utils.maybe_update_config_version(cfg)
      self.world_size = 1
      if trainer is not None:
          self.world_size = trainer.num_nodes * trainer.num_devices
  
      super().__init__(cfg=cfg, trainer=trainer)
  
      # Expected sample rate for the input audio
      self.sample_rate = cfg.sample_rate
  
      # Number of samples in each audio frame that is encoded
      self.samples_per_frame = cfg.samples_per_frame
  
      # Discriminator updates
      self.disc_updates_per_period = cfg.get(
          "disc_updates_per_period", 1
      )
      self.disc_update_period = cfg.get("disc_update_period", 1)
      if self.disc_updates_per_period > self.disc_update_period:
          raise ValueError(
              f"Number of discriminator updates ({self.disc_updates_per_period}) per period must be less or equal to the configured period ({self.disc_update_period})"
          )
  
      # Encoder setup
      self.audio_encoder = instantiate(cfg.audio_encoder)
  
      # Optionally, add gaussian noise to encoder output as an information bottleneck
      encoder_noise_stdev = cfg.get("encoder_noise_stdev", 0.0)
      if encoder_noise_stdev:
          self.encoder_noise = GaussianDropout(
              stdev=encoder_noise_stdev
          )
      else:
          self.encoder_noise = None
  
      if "vector_quantizer" in cfg:
          self.vector_quantizer = instantiate(cfg.vector_quantizer)
  
          vq_output_types = list(
              self.vector_quantizer.output_types.keys()
          )
  
          if (
              len(vq_output_types) == 3
              and vq_output_types[-1] == "commit_loss"
          ):
              self.vector_quantizer_has_commit_loss = True
              logging.info("Vector quantizer supports commit loss.")
          else:
              self.vector_quantizer_has_commit_loss = False
              logging.info(
                  "Vector quantizer does not support commit loss."
              )
  
      else:
          logging.warning("Vector quantizer will not be used.")
          self.vector_quantizer = None
  
      # Decoder setup
      self.audio_decoder = instantiate(cfg.audio_decoder)
  
      # Discriminator setup
      self.discriminator = instantiate(cfg.discriminator)
  
      # Mel loss setup
      loss_resolutions = cfg.loss_resolutions
      mel_loss_dims = cfg.get("mel_loss_dims")
      mel_loss_log_guard = cfg.get("mel_loss_log_guard", 1.0)
      self.mel_loss_l1_scale = cfg.get("mel_loss_l1_scale", 1.0)
      self.mel_loss_l2_scale = cfg.get("mel_loss_l2_scale", 1.0)
      self.mel_loss_fn = MultiResolutionMelLoss(
          sample_rate=self.sample_rate,
          mel_dims=mel_loss_dims,
          resolutions=loss_resolutions,
          log_guard=mel_loss_log_guard,
      )
  
      # STFT loss setup
      stft_loss_log_guard = cfg.get("stft_loss_log_guard", 1.0)
      self.stft_loss_scale = cfg.get("stft_loss_scale", 0.0)
      self.stft_loss_fn = MultiResolutionSTFTLoss(
          resolutions=loss_resolutions,
          log_guard=stft_loss_log_guard,
      )
  
      # Time domain loss setup
      self.time_domain_loss_scale = cfg.get(
          "time_domain_loss_scale", 1.0
      )
      self.si_sdr_loss_scale = cfg.get("si_sdr_loss_scale", 0.0)
      self.time_domain_loss_fn = TimeDomainLoss()
      self.si_sdr_loss_fn = SISDRLoss()
  
      # Discriminator loss setup
      self.gen_loss_scale = cfg.get("gen_loss_scale", 1.0)
      self.feature_loss_scale = cfg.get("feature_loss_scale", 1.0)
      self.gen_loss_fn = instantiate(cfg.generator_loss)
      self.disc_loss_fn = instantiate(cfg.discriminator_loss)
      self.feature_loss_fn = RelativeFeatureMatchingLoss()
  
      # Codebook loss setup
      if self.vector_quantizer:
          self.commit_loss_scale = cfg.get("commit_loss_scale", 1.0)
      else:
          self.commit_loss_scale = 0.0
  
      if (
          self.commit_loss_scale > 0
          and not self.vector_quantizer_has_commit_loss
      ):
          raise ValueError(
              "Commit loss is enabled but the quantizer does not support it."
          )
  
      # Log setup
      self.log_config = cfg.get("log_config", None)
  
      # Optimizer setup
      self.lr_schedule_interval = None
      self.automatic_optimization = False
  
imports: import itertools
  from math import ceil
  from pathlib import Path
  from typing import List, Tuple
  import torch
  import torch.nn.functional
  from einops import rearrange
  from hydra.utils import instantiate
  from omegaconf import DictConfig, OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from nemo.collections.tts.losses.audio_codec_loss import MultiResolutionMelLoss, MultiResolutionSTFTLoss, RelativeFeatureMatchingLoss, SISDRLoss, TimeDomainLoss
  from nemo.collections.tts.modules.common import GaussianDropout
  from nemo.collections.tts.parts.utils.callbacks import LoggingCallback
  from nemo.collections.tts.parts.utils.helpers import get_batch_size, get_num_workers
  from nemo.core import ModelPT
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.neural_types.elements import AudioSignal, EncodedRepresentation, LengthsType, TokenIndex
  from nemo.core.neural_types.neural_type import NeuralType
  from nemo.core.optim.lr_scheduler import compute_max_steps, prepare_lr_scheduler
  from nemo.utils import logging, model_utils
  from nemo.utils.decorators import experimental
label_desc: Use image node_id 1 to create a new AudioCodecModel object from inherited base classes: ModelPT with example: obj = AudioCodecModel(cfg, trainer)