class_docstr: Class for encoder-mask-decoder audio processing models.
  
  The model consists of the following blocks:
      - encoder: transforms input multi-channel audio signal into an encoded representation (analysis transform)
      - mask_estimator: estimates a mask used by signal processor
      - mask_processor: mask-based signal processor, combines the encoded input and the estimated mask
      - decoder: transforms processor output into the time domain (synthesis transform)
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docstr: Sets up the training data loader via a Dict-like object.
  
  Args:
      train_data_config: A config that contains the information regarding construction
          of a training dataset.
  
  Supported Datasets:
      -   :class:`~nemo.collections.asr.data.audio_to_audio.AudioToTargetDataset`
docstr_tok: ["Sets","up","the","training","data","loader","via","a","Dict-like","object",".","Args",":","train_data_config",":","A","config","that","contains","the","information","regarding","construction","of","a","training","dataset",".","Supported","Datasets",":","-",":","class",":","`","~nemo.collections.asr.data.audio_to_audio.AudioToTargetDataset","`"]
code: def setup_training_data(
      self, train_data_config: Optional[Union[DictConfig, Dict]]
  ):
      """
      Sets up the training data loader via a Dict-like object.
  
      Args:
          train_data_config: A config that contains the information regarding construction
              of a training dataset.
  
      Supported Datasets:
          -   :class:`~nemo.collections.asr.data.audio_to_audio.AudioToTargetDataset`
      """
      if "shuffle" not in train_data_config:
          train_data_config["shuffle"] = True
  
      # preserve config
      self._update_dataset_config(
          dataset_name="train", config=train_data_config
      )
  
      self._train_dl = self._setup_dataloader_from_config(
          config=train_data_config
      )
  
      if (
          "is_tarred" in train_data_config
          and train_data_config["is_tarred"]
      ):
          raise NotImplementedError("Tarred datasets not supported")
  
imports: import json
  import os
  import tempfile
  from typing import Dict, List, Optional, Union
  import librosa
  import soundfile
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from tqdm import tqdm
  from nemo.collections.asr.data import audio_to_audio_dataset
  from nemo.collections.asr.data.audio_to_text_dataset import inject_dataloader_value_from_model_config
  from nemo.collections.asr.models.audio_to_audio_model import AudioToAudioModel
  from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.neural_types import AudioSignal, LengthsType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the EncMaskDecAudioToAudioModel obj's underlying member method code with example usage: obj.setup_training_data(train_data_config) without return types