class_docstr: A mixin for pretrained models that makes it possible to specify an encoding pipeline and a decoding pipeline
class_docstr_tok: ["A","mixin","for","pretrained","models","that","makes","it","possible","to","specify","an","encoding","pipeline","and","a","decoding","pipeline"]
docstr: Retrieves the value associated with the specified key, dereferencing
  .data where applicable
  
  Arguments
  ---------
  data: PaddedBatch
      a padded batch
  key: str
      the key
  
  Returns
  -------
  result: object
      the result
code: def _get_value(self, data, key):
      """
      Retrieves the value associated with the specified key, dereferencing
      .data where applicable
  
      Arguments
      ---------
      data: PaddedBatch
          a padded batch
      key: str
          the key
  
      Returns
      -------
      result: object
          the result
      """
      value = getattr(data, key)
      if not self.input_use_padded_data and isinstance(
          value, PaddedData
      ):
          value = value.data
      return value
  
imports: import logging
  import hashlib
  import sys
  import warnings
  import torch
  import torchaudio
  from types import SimpleNamespace
  from torch.nn import SyncBatchNorm
  from torch.nn import DataParallel
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.fetching import fetch
  from speechbrain.dataio.preprocess import AudioNormalizer
  from torch.nn.parallel import DistributedDataParallel
  from speechbrain.utils.data_utils import split_path
  from speechbrain.utils.distributed import run_on_main
  from speechbrain.dataio.batch import PaddedBatch, PaddedData
  from speechbrain.utils.data_pipeline import DataPipeline
  from speechbrain.utils.superpowers import import_from_path
label_desc: Use image node_id 8 for calling the EncodeDecodePipelineMixin obj's underlying member method code with example usage: obj._get_value(data, key) and returns: value