code: def setup_training_data(self, cfg):
      self._train_dl = self._setup_train_dataloader(cfg)
      batch_size = cfg["dataloader_params"]["batch_size"]
      # Need to set this because if using an IterableDataset, the length of the dataloader is the total number
      # of samples rather than the number of batches, and this messes up the tqdm progress bar.
      # So we set the number of steps manually (to the correct number) to fix this.
      if (
          self._train_dl is not None
          and hasattr(self._train_dl, "dataset")
          and isinstance(
              self._train_dl.dataset, torch.utils.data.IterableDataset
          )
      ):
          # We also need to check if limit_train_batches is already set.
          # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches,
          # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0).
          if self._trainer is not None and isinstance(
              self._trainer.limit_train_batches, float
          ):
              self._trainer.limit_train_batches = int(
                  self._trainer.limit_train_batches
                  * ceil(
                      (len(self._train_dl.dataset) / self.world_size)
                      / batch_size
                  )
              )
          elif self._trainer is None:
              logging.warning(
                  "Model Trainer was not set before constructing the dataset, incorrect number of "
                  "training batches will be used. Please set the trainer and rebuild the dataset."
              )
  
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 19 for calling the AudioCodecModel obj's underlying member method code with example usage: obj.setup_training_data(cfg) without return types