code: def __getitem__(self, item):
      c_supervision = self.supervisions[item]
      start = c_supervision.start
      duration = c_supervision.duration
      c_recordings = recordings[c_supervision.recording_id]
      fs = c_recordings.sampling_rate
      all_channels, chan_indx = self._get_read_chans(
          c_supervision, c_recordings, start, duration, fs
      )
  
      assert all_channels.ndim == 3
      assert (
          all_channels.shape[0] == 1
      ), "If batch size is more than one here something went wrong."
      with torch.inference_mode():
          c_scores = ranker(all_channels)
      c_scores = c_scores[0].numpy().tolist()
      c_scores = [(x, y) for x, y in zip(c_scores, chan_indx)]
      c_scores = sorted(c_scores, key=lambda x: x[0], reverse=True)
      c_scores = c_scores[: int(len(c_scores) * self.top_k)]
      new_sup = deepcopy(c_supervision)
      new_sup.channel = [x[-1] for x in c_scores]
      return new_sup
  
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 4 for calling the MicRanking obj's underlying member method code with example usage: obj.__getitem__(item) and returns: new_sup