code: def diffwave_mel_spectogram(
      sample_rate,
      hop_length,
      win_length,
      n_fft,
      n_mels,
      f_min,
      f_max,
      power,
      normalized,
      norm,
      mel_scale,
      audio,
  ):
      """calculates MelSpectrogram for a raw audio signal
      and preprocesses it for diffwave training
  
      Arguments
      ---------
      sample_rate : int
          Sample rate of audio signal.
      hop_length : int
          Length of hop between STFT windows.
      win_length : int
          Window size.
      n_fft : int
          Size of FFT.
      n_mels : int
          Number of mel filterbanks.
      f_min : float
          Minimum frequency.
      f_max : float
          Maximum frequency.
      power : float
          Exponent for the magnitude spectrogram.
      normalized : bool
          Whether to normalize by magnitude after stft.
      norm : str or None
          If "slaney", divide the triangular mel weights by the width of the mel band
      mel_scale : str
          Scale to use: "htk" or "slaney".
      audio : torch.tensor
          input audio signal
      """
      audio_to_mel = transforms.MelSpectrogram(
          sample_rate=sample_rate,
          hop_length=hop_length,
          win_length=win_length,
          n_fft=n_fft,
          n_mels=n_mels,
          f_min=f_min,
          f_max=f_max,
          power=power,
          normalized=normalized,
          norm=norm,
          mel_scale=mel_scale,
      ).to(audio.device)
  
      mel = audio_to_mel(torch.clamp(audio, -1.0, 1.0))
      mel = 20 * torch.log10(torch.clamp(mel, min=1e-5)) - 20
      mel = torch.clamp((mel + 100) / 100, 0.0, 1.0)
      return mel
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from speechbrain.nnet.CNN import Conv1d
  from speechbrain.nnet import linear
  from speechbrain.nnet.diffusion import DenoisingDiffusion
  from math import sqrt
  from torchaudio import transforms
label_desc: Use image node_id 2 for calling a global function with example usage: diffwave_mel_spectogram(sample_rate, hop_length, win_length, n_fft, n_mels, f_min, f_max, power, normalized, norm, mel_scale, audio) and returns: mel