code: def forward(self, inp):
      if (
          len(inp.shape) == 3
      ):  # Automatically squeeze out the channels dimension if it is present (assuming mono-audio)
          inp = inp.squeeze(1)
      assert len(inp.shape) == 2
      self.mel_stft = self.mel_stft.to(inp.device)
      mel = self.mel_stft(inp)
      # Perform dynamic range compression
      mel = torch.log(torch.clamp(mel, min=1e-5))
      if self.mel_norms is not None:
          self.mel_norms = self.mel_norms.to(mel.device)
          mel = mel / self.mel_norms.unsqueeze(0).unsqueeze(-1)
      return mel
  
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 2 for calling the TorchMelSpectrogram obj's underlying member method code with example usage: obj.forward(inp) and returns: mel