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: Undoes the normalization performed on spectrograms
  
  Arguments
  ---------
  samples: torch.Tensor
      normalized samples
  
  Returns
  -------
  result: torch.Tensor
      denormalized samples
docstr_tok: ["Undoes","the","normalization","performed","on","spectrograms","Arguments","--","--","--","--","-","samples",":","torch.Tensor","normalized","samples","Returns","--","--","--","-","result",":","torch.Tensor","denormalized","samples"]
code: def denormalize(self, samples):
      """Undoes the normalization performed on spectrograms
  
      Arguments
      ---------
      samples: torch.Tensor
          normalized samples
  
      Returns
      -------
      result: torch.Tensor
          denormalized samples"""
      if not torch.is_tensor(samples):
          samples = torch.stack(samples)
      samples = samples[:, :, :, : self.hparams.spec_n_mels]
      samples = self.modules.min_level_norm.denormalize(samples)
      samples = AF.DB_to_amplitude(
          samples, ref=self.hparams.spec_ref, power=1.0
      )
      samples = self.modules.dynamic_range_compression(samples)
      return samples
  
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 30 for calling the DiffusionBrain obj's underlying member method code with example usage: obj.denormalize(samples) and returns: samples