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: Initializes the encode and decode pipeline
code: def create_pipelines(self):
      """
      Initializes the encode and decode pipeline
      """
      self._run_init_steps(self.hparams.encode_pipeline)
      self._run_init_steps(self.hparams.decode_pipeline)
      self.encode_pipeline = DataPipeline(
          static_data_keys=self.INPUT_STATIC_KEYS,
          dynamic_items=self.hparams.encode_pipeline["steps"],
          output_keys=self.hparams.encode_pipeline["output_keys"],
      )
      self.decode_pipeline = DataPipeline(
          static_data_keys=self.hparams.model_output_keys,
          dynamic_items=self.hparams.decode_pipeline["steps"],
          output_keys=self.OUTPUT_KEYS,
      )
  
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 1 for calling the EncodeDecodePipelineMixin obj's underlying member method code with example usage: obj.create_pipelines() without return types