code: def foreign_class(
      source,
      hparams_file="hyperparams.yaml",
      pymodule_file="custom.py",
      classname="CustomInterface",
      overrides={},
      overrides_must_match=True,
      savedir=None,
      use_auth_token=False,
      download_only=False,
      huggingface_cache_dir=None,
      **kwargs,
  ):
      """Fetch and load an interface from an outside source
  
      The source can be a location on the filesystem or online/huggingface
  
      The pymodule file should contain a class with the given classname. An
      instance of that class is returned. The idea is to have a custom Pretrained
      subclass in the file. The pymodule file is also added to the python path
      before the Hyperparams YAML file is loaded, so it can contain any custom
      implementations that are needed.
  
      The hyperparams file should contain a "modules" key, which is a
      dictionary of torch modules used for computation.
  
      The hyperparams file should contain a "pretrainer" key, which is a
      speechbrain.utils.parameter_transfer.Pretrainer
  
      Arguments
      ---------
      source : str or Path or FetchSource
          The location to use for finding the model. See
          ``speechbrain.pretrained.fetching.fetch`` for details.
      hparams_file : str
          The name of the hyperparameters file to use for constructing
          the modules necessary for inference. Must contain two keys:
          "modules" and "pretrainer", as described.
      pymodule_file : str
          The name of the Python file that should be fetched.
      classname : str
          The name of the Class, of which an instance is created and returned
      overrides : dict
          Any changes to make to the hparams file when it is loaded.
      overrides_must_match : bool
          Whether an error will be thrown when an override does not match
          a corresponding key in the yaml_stream.
      savedir : str or Path
          Where to put the pretraining material. If not given, will use
          ./pretrained_models/<class-name>-hash(source).
      use_auth_token : bool (default: False)
          If true Hugginface's auth_token will be used to load private models from the HuggingFace Hub,
          default is False because the majority of models are public.
      download_only : bool (default: False)
          If true, class and instance creation is skipped.
      huggingface_cache_dir : str
          Path to HuggingFace cache; if None -> "~/.cache/huggingface" (default: None)
  
      Returns
      -------
      object
          An instance of a class with the given classname from the given pymodule file.
      """
      if savedir is None:
          savedir = f"./pretrained_models/{classname}-{hashlib.md5(source.encode('UTF-8', errors='replace')).hexdigest()}"
      hparams_local_path = fetch(
          filename=hparams_file,
          source=source,
          savedir=savedir,
          overwrite=False,
          save_filename=None,
          use_auth_token=use_auth_token,
          revision=None,
          huggingface_cache_dir=huggingface_cache_dir,
      )
      pymodule_local_path = fetch(
          filename=pymodule_file,
          source=source,
          savedir=savedir,
          overwrite=False,
          save_filename=None,
          use_auth_token=use_auth_token,
          revision=None,
          huggingface_cache_dir=huggingface_cache_dir,
      )
      sys.path.append(str(pymodule_local_path.parent))
  
      # Load the modules:
      with open(hparams_local_path) as fin:
          hparams = load_hyperpyyaml(
              fin, overrides, overrides_must_match
          )
  
      # Pretraining:
      pretrainer = hparams["pretrainer"]
      pretrainer.set_collect_in(savedir)
      # For distributed setups, have this here:
      run_on_main(
          pretrainer.collect_files, kwargs={"default_source": source}
      )
      # Load on the CPU. Later the params can be moved elsewhere by specifying
      if not download_only:
          # run_opts={"device": ...}
          pretrainer.load_collected()
  
          # Import class and create instance
          module = import_from_path(pymodule_local_path)
          cls = getattr(module, classname)
          return cls(
              modules=hparams["modules"], hparams=hparams, **kwargs
          )
  
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 a global function with example usage: foreign_class(source, hparams_file, pymodule_file, classname, overrides, overrides_must_match, savedir, use_auth_token, download_only, huggingface_cache_dir) and returns: cls