class_docstr: Envelope Variance Channel Selection method with
  (optionally) learnable per mel-band weights.
code: def forward(self, channels):
      assert channels.ndim == 3
      mels = self.mels(channels)
      if mels.shape[-1] > (self.chunk_size + self.chunk_stride):
          # using for because i am too lazy of taking care of padded
          # values in stats computation, but this is fast
  
          indxs = self._get_chunks_indx(
              mels.shape[-1], self.chunk_size, self.chunk_stride
          )
          all_win_ranks = [
              self._single_window(mels[..., s:t]) for s, t in indxs
          ]
  
          return torch.stack(all_win_ranks).mean(0)
      else:
          return self._single_window(mels)
  
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 5 for calling the EnvelopeVariance obj's underlying member method code with example usage: obj.forward(channels) and returns: torch, self