code: def __init__(
      self,
      training_files,
      segment_size,
      n_fft,
      num_mels,
      hop_size,
      win_size,
      sampling_rate,
      fmin,
      fmax,
      split=True,
      shuffle=True,
      n_cache_reuse=1,
      device=None,
      fmax_loss=None,
      fine_tuning=False,
      base_mels_path=None,
  ):
      self.audio_files = training_files
      random.seed(1234)
      if shuffle:
          random.shuffle(self.audio_files)
      self.segment_size = segment_size
      self.sampling_rate = sampling_rate
      self.split = split
      self.n_fft = n_fft
      self.num_mels = num_mels
      self.hop_size = hop_size
      self.win_size = win_size
      self.fmin = fmin
      self.fmax = fmax
      self.fmax_loss = fmax_loss
      self.cached_wav = None
      self.n_cache_reuse = n_cache_reuse
      self._cache_ref_count = 0
      self.device = device
      self.fine_tuning = fine_tuning
      self.base_mels_path = base_mels_path
  
imports: import math
  import os
  import random
  import torch
  import torch.utils.data
  import numpy
  from librosa.util import normalize
  from scipy.io.wavfile import read
  from librosa.filters import mel
label_desc: Use image node_id 1 to create a new MelDataset object from inherited base classes: torch.utils.data with example: obj = MelDataset(training_files, segment_size, n_fft, num_mels, hop_size, win_size, sampling_rate, fmin, fmax, split, shuffle, n_cache_reuse, device, fmax_loss, fine_tuning, base_mels_path)