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: Decodes the raw model outputs
  
  Arguments
  ---------
  output: tuple
      raw model outputs
  
  Returns
  -------
  result: dict or list
      the output of the pipeline
code: def decode_output(self, output):
      """
      Decodes the raw model outputs
  
      Arguments
      ---------
      output: tuple
          raw model outputs
  
      Returns
      -------
      result: dict or list
          the output of the pipeline
      """
      pipeline_input = self._get_decode_pipeline_input(output)
      return self._run_pipeline(
          pipeline=self.decode_pipeline,
          input=pipeline_input,
          batch=self.batch_outputs,
      )
  
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 14 for calling the EncodeDecodePipelineMixin obj's underlying member method code with example usage: obj.decode_output(output) and returns: self