code: def get_prediction(repo, values, updates_dir=None):
      """Gets the prediction for one predefined audio example, pattern: {repo}/{values["sample"]} (see HF model card).
  
      Parameters
      ----------
      repo: str
          Source of pretrained model (assuming its within the HF speechbrain collection).
      values: dict
          Interface specification.
          Examples: speechbrain:hf-interface-testing/updates_pretrained_models/ssl-wav2vec2-base-librispeech/test.yaml
                    speechbrain:hf-interface-testing/updates_pretrained_models/asr-wav2vec2-librispeech/test.yaml
      updates_dir: str
          Controls whether/not we are in the refactored results (None: expected results; before refactoring).
  
      Returns
      -------
      Cleaned-up prediction results for yaml output (result logging & comparison through yaml de/serialization).
      """
  
      def sanitize(data):
          # cleanup data for yaml output (w/o this, yaml will make attempts to save torch/numpy arrays in their format)
          if isinstance(data, torch.Tensor):
              data = data.detach().cpu().numpy()
              if data.ndim:
                  data = list(data)
          return data
  
      # get the pretrained model (before/after yaml/interface update)
      model = get_model(
          repo=repo, values=values, updates_dir=updates_dir
      )  # noqa
  
      try:
          # simulate batch from single file
          prediction = eval(
              f'model.{values["fnx"]}(model.load_audio("{repo}/{values["sample"]}", savedir="pretrained_models/{repo}").unsqueeze(0), torch.tensor([1.0]))'
          )
  
      except Exception:
          # use an example audio if no audio can be loaded
          print(
              f'\tWARNING - no audio found on HF: {repo}/{values["sample"]}'
          )
          prediction = eval(
              f'model.{values["fnx"]}(model.load_audio("tests/samples/single-mic/example1.wav", savedir="pretrained_models/{repo}").unsqueeze(0), torch.tensor([1.0]))'
          )
  
      finally:
          del model
  
      return [sanitize(x[0]) for x in prediction]
  
imports: import os
  import sys
  from tqdm import tqdm
  import yaml
  import torch
  import importlib
  import subprocess
  import speechbrain
  from glob import glob
  from copy import deepcopy
  from torch.utils.data import DataLoader
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.distributed import run_on_main
  from speechbrain.utils.train_logger import FileTrainLogger
  from speechbrain.inference.interfaces import foreign_class
  from speechbrain.dataio.dataloader import LoopedLoader, make_dataloader
label_desc: Use image node_id 3 for calling a global function with example usage: get_prediction(repo, values, updates_dir) and returns: unknown, data