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: Prepare a dataloader for processing files.
  
  Args:
      config: A python dictionary which contains the following keys:
          manifest_filepath: path to a manifest file
          input_key: key with audio filepaths in the manifest
          input_channel_selector: Optional, used to select a subset of channels from input audio files
          batch_size: batch size for the dataloader
          num_workers: number of workers for the dataloader
  
  Returns:
      A pytorch DataLoader for the given manifest filepath.
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code: def _setup_process_dataloader(
      self, config: Dict
  ) -> "torch.utils.data.DataLoader":
      """Prepare a dataloader for processing files.
  
      Args:
          config: A python dictionary which contains the following keys:
              manifest_filepath: path to a manifest file
              input_key: key with audio filepaths in the manifest
              input_channel_selector: Optional, used to select a subset of channels from input audio files
              batch_size: batch size for the dataloader
              num_workers: number of workers for the dataloader
  
      Returns:
          A pytorch DataLoader for the given manifest filepath.
      """
      dl_config = {
          "manifest_filepath": config["manifest_filepath"],
          "sample_rate": self.sample_rate,
          "input_key": config["input_key"],
          "input_channel_selector": config.get(
              "input_channel_selector", None
          ),
          "target_key": None,
          "target_channel_selector": None,
          "batch_size": config["batch_size"],
          "shuffle": False,
          "num_workers": config.get(
              "num_workers",
              min(config["batch_size"], os.cpu_count() - 1),
          ),
          "pin_memory": True,
      }
  
      temporary_dataloader = self._setup_dataloader_from_config(
          config=DictConfig(dl_config)
      )
      return temporary_dataloader
  
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 7 for calling the EncMaskDecAudioToAudioModel obj's underlying member method code with example usage: obj._setup_process_dataloader(config) and returns: temporary_dataloader