class_docstr: Class that manages the training loop. See speechbrain.core.Brain.
  
  Arguments
  ---------
  modules : dict of str:torch.nn.Module pairs
      These modules are passed to the optimizer by default if they have
      trainable parameters, and will have ``train()``/``eval()`` called on them.
  opt_class : torch.optim class
      A torch optimizer constructor that takes only the list of
      parameters (e.g. a lambda or partial function definition). By default,
      this will be passed all modules in ``modules`` at the
      beginning of the ``fit()`` method. This behavior can be changed
      by overriding the ``configure_optimizers()`` method.
  hparams : dict
      Each key:value pair should consist of a string key and a hyperparameter
      that is used within the overridden methods. These will
      be accessible via an ``hparams`` attribute, using "dot" notation:
      e.g., self.hparams.model(x).
  run_opts : dict
      A set of options to change the runtime environment.
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docstr: Gets a filename label for the specified sample
  
  Arguments
  ---------
  label: dict
      a dictionary similar to the following:
      {"digit": 4, "speaker": 10}
  idx: int
      the item index (will be appended)
  
  Returns
  -------
  result: str
      a formatted label. For the example above, it will
      be "digit_4_speaker_10"
docstr_tok: ["Gets","a","filename","label","for","the","specified","sample","Arguments","--","--","--","--","-","label",":","dict","a","dictionary","similar","to","the","following",":","{","``","digit","''",":","4",",","``","speaker","''",":","10","}","idx",":","int","the","item","index","(","will","be","appended",")","Returns","--","--","--","-","result",":","str","a","formatted","label",".","For","the","example","above",",","it","will","be","``","digit_4_speaker_10","''"]
code: def get_sample_label(self, label, idx):
      """Gets a filename label for the specified sample
  
      Arguments
      ---------
      label: dict
          a dictionary similar to the following:
          {"digit": 4, "speaker": 10}
      idx: int
          the item index (will be appended)
  
      Returns
      -------
      result: str
          a formatted label. For the example above, it will
          be "digit_4_speaker_10"
      """
      label_str = "_".join(
          f"{key}_{value}" for key, value in label.items()
      )
      return f"{label_str}_{idx}"
  
imports: import logging
  import sys
  import torch
  import speechbrain
  import os
  from collections import namedtuple
  from audiomnist_prepare import prepare_audiomnist
  from hyperpyyaml import load_hyperpyyaml
  from torchaudio import functional
  from speechbrain.dataio.dataio import length_to_mask, write_audio
  from speechbrain.dataio.dataset import apply_overfit_test
  from speechbrain.utils import data_utils
  from speechbrain.utils.train_logger import plot_spectrogram
  from enum import Enum
  from speechbrain.utils.distributed import run_on_main
  from speechbrain.utils.data_utils import dict_value_combinations, dist_stats, masked_mean, masked_min, masked_max, masked_std, match_shape
label_desc: Use image node_id 25 for calling the DiffusionBrain obj's underlying member method code with example usage: obj.get_sample_label(label, idx) and returns: label_str, str, idx