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: Evaluate one batch, override for different procedure than train.
  
  The default implementation depends on two methods being defined
  with a particular behavior:
  
  * ``compute_forward()``
  * ``compute_objectives()``
  
  Arguments
  ---------
  batch : list of torch.Tensors
      Batch of data to use for evaluation. Default implementation assumes
      this batch has two elements: inputs and targets.
  stage : Stage
      The stage of the experiment: Stage.VALID, Stage.TEST
  
  Returns
  -------
  detached loss
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code: def evaluate_batch(self, batch, stage):
      """Evaluate one batch, override for different procedure than train.
  
      The default implementation depends on two methods being defined
      with a particular behavior:
  
      * ``compute_forward()``
      * ``compute_objectives()``
  
      Arguments
      ---------
      batch : list of torch.Tensors
          Batch of data to use for evaluation. Default implementation assumes
          this batch has two elements: inputs and targets.
      stage : Stage
          The stage of the experiment: Stage.VALID, Stage.TEST
  
      Returns
      -------
      detached loss
      """
  
      out = self.compute_forward(batch, stage=stage)
      loss, _, _ = self.compute_objectives(out, batch, stage=stage)
      return loss.detach().cpu()
  
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 9 for calling the DiffusionBrain obj's underlying member method code with example usage: obj.evaluate_batch(batch, stage) and returns: loss