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: Determines whether the input pipeline
  operates on batches or individual examples
  (true means batched)
  
  Returns
  -------
  batch_inputs: bool
code: def batch_inputs(self):
      """
      Determines whether the input pipeline
      operates on batches or individual examples
      (true means batched)
  
      Returns
      -------
      batch_inputs: bool
      """
      return self.hparams.encode_pipeline.get("batch", True)
  
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 9 for calling the EncodeDecodePipelineMixin obj's underlying member method code with example usage: obj.batch_inputs() and returns: self