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"]
code: def _get_decode_pipeline_input(self, model_output):
      model_output_keys = getattr(
          self.hparams, "model_output_keys", None
      )
      pipeline_input = model_output
      if len(model_output_keys) == 1:
          pipeline_input = (pipeline_input,)
      # The input to a pipeline is a dictionary. If model_output_keys
      # is provided, the output of the model is assumed to be a collection
      # (e.g. a list or a tuple).
      if model_output_keys:
          pipeline_input = dict(zip(model_output_keys, pipeline_input))
  
      # By default, the pipeline will be applied to in batch mode
      # to the entire model input
      if not self.batch_outputs:
          pipeline_input = self._itemize(pipeline_input)
      return pipeline_input
  
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 5 for calling the EncodeDecodePipelineMixin obj's underlying member method code with example usage: obj._get_decode_pipeline_input(model_output) and returns: pipeline_input