class_docstr: Class that manages the training loop. See speechbrain.core.Brain.
class_docstr_tok: ["Class","that","manages","the","training","loop",".","See","speechbrain.core.Brain","."]
docstr: Remembers samples of spectrograms and the batch for logging purposes
  Arguments
  ---------
  batch: tuple
      a training batch
  predictions: tuple
      predictions (raw output of the Tacotron model)
docstr_tok: ["Remembers","samples","of","spectrograms","and","the","batch","for","logging","purposes","Arguments","--","--","--","--","-","batch",":","tuple","a","training","batch","predictions",":","tuple","predictions","(","raw","output","of","the","Tacotron","model",")"]
code: def _remember_sample(self, batch, predictions):
      """Remembers samples of spectrograms and the batch for logging purposes
      Arguments
      ---------
      batch: tuple
          a training batch
      predictions: tuple
          predictions (raw output of the Tacotron model)
      """
      mel, sig = batch
      pred, noise, noisy_sample, steps = predictions
  
file_path: speechbrain/recipes/LJSpeech/TTS/vocoder/diffwave/train.py
imports: import torchaudio
  import logging
  import sys
  import torch
  import speechbrain
  import os
  from hyperpyyaml import load_hyperpyyaml
label_desc: Use image node_id 4 for calling the DiffWaveBrain obj's underlying member method code with example usage: obj._remember_sample(batch, predictions) without return types