class_docstr: Envelope Variance Channel Selection method with
  (optionally) learnable per mel-band weights.
code: def _single_window(self, mels):
      logmels = torch.log(mels + self.eps)
      mels = torch.exp(logmels - torch.mean(logmels, -1, keepdim=True))
      var = torch.var(mels ** (1 / 3), dim=-1)  # channels, subbands
      var = var / torch.amax(var, 1, keepdim=True)
      subband_weights = torch.abs(self.subband_weights)
      ranking = torch.sum(var * subband_weights, -1)
      return ranking
  
imports: import argparse
  import os
  from copy import deepcopy
  from pathlib import Path
  import lhotse
  import soundfile
  import torch
  import torchaudio
  import tqdm
  from torch.utils.data import DataLoader, Dataset
label_desc: Use image node_id 2 for calling the EnvelopeVariance obj's underlying member method code with example usage: obj._single_window(mels) and returns: ranking