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: Encodes the inputs using the pipeline
  
  Arguments
  ---------
  input: dict
      the raw inputs
  
  Returns
  -------
  results: object
code: def encode_input(self, input):
      """
      Encodes the inputs using the pipeline
  
      Arguments
      ---------
      input: dict
          the raw inputs
  
      Returns
      -------
      results: object
  
      """
      pipeline_input = self._get_encode_pipeline_input(input)
      model_input = self._run_pipeline(
          pipeline=self.encode_pipeline,
          input=pipeline_input,
          batch=self.batch_inputs,
      )
      model_input = self._collate(model_input)
      if hasattr(model_input, "to"):
          model_input = model_input.to(self.device)
      return self.to_dict(model_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 13 for calling the EncodeDecodePipelineMixin obj's underlying member method code with example usage: obj.encode_input(input) and returns: self