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: Converts padded batches to dictionaries, leaves
  other data types as is
  
  Arguments
  ---------
  data: object
      a dictionary or a padded batch
  
  Returns
  -------
  results: dict
      the dictionary
code: def to_dict(self, data):
      """
      Converts padded batches to dictionaries, leaves
      other data types as is
  
      Arguments
      ---------
      data: object
          a dictionary or a padded batch
  
      Returns
      -------
      results: dict
          the dictionary
      """
      if isinstance(data, PaddedBatch):
          data = {
              key: self._get_value(data, key)
              for key in self.hparams.encode_pipeline["output_keys"]
          }
      return data
  
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 7 for calling the EncodeDecodePipelineMixin obj's underlying member method code with example usage: obj.to_dict(data) and returns: data