code: def __init__(
      self,
      filter_length=1024,
      hop_length=256,
      win_length=1024,
      n_mel_channels=80,
      mel_fmin=0,
      mel_fmax=8000,
      sampling_rate=22050,
      normalize=False,
      mel_norm_file=DEFAULT_MEL_NORM_FILE,
  ):
      super().__init__()
      # These are the default tacotron values for the MEL spectrogram.
      self.filter_length = filter_length
      self.hop_length = hop_length
      self.win_length = win_length
      self.n_mel_channels = n_mel_channels
      self.mel_fmin = mel_fmin
      self.mel_fmax = mel_fmax
      self.sampling_rate = sampling_rate
      self.mel_stft = torchaudio.transforms.MelSpectrogram(
          n_fft=self.filter_length,
          hop_length=self.hop_length,
          win_length=self.win_length,
          power=2,
          normalized=normalize,
          sample_rate=self.sampling_rate,
          f_min=self.mel_fmin,
          f_max=self.mel_fmax,
          n_mels=self.n_mel_channels,
          norm="slaney",
      )
      self.mel_norm_file = mel_norm_file
      if self.mel_norm_file is not None:
          with fsspec.open(self.mel_norm_file) as f:
              self.mel_norms = torch.load(f)
      else:
          self.mel_norms = None
  
imports: import functools
  import math
  import os
  import fsspec
  import torch
  import torch.nn
  import torch.nn.functional
  import torchaudio
  from transformers import LogitsWarper
  from TTS.tts.layers.tortoise.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
label_desc: Use image node_id 1 to create a new TorchMelSpectrogram object from inherited base classes: nn with example: obj = TorchMelSpectrogram(filter_length, hop_length, win_length, n_mel_channels, mel_fmin, mel_fmax, sampling_rate, normalize, mel_norm_file)