code: def compute_embeddings(wavs, lens):
      """Definition of the steps for computation of embeddings from the waveforms."""
      with torch.no_grad():
          wavs = wavs.to(run_opts["device"])
          feats = params["compute_features"](wavs)
          feats = params["mean_var_norm"](feats, lens)
          emb = params["embedding_model"](feats, lens)
          emb = params["mean_var_norm_emb"](
              emb, torch.ones(emb.shape[0], device=run_opts["device"])
          )
  
      return emb
  
imports: import os
  import sys
  import torch
  import logging
  import pickle
  import json
  import glob
  import shutil
  import numpy
  import speechbrain
  from tqdm.contrib import tqdm
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.distributed import run_on_main
  from speechbrain.processing.PLDA_LDA import StatObject_SB
  from speechbrain.processing import diarization
  from speechbrain.utils.DER import DER
  from speechbrain.dataio.dataio import read_audio
  from speechbrain.dataio.dataio import read_audio_multichannel
label_desc: Use image node_id 1 for calling a global function with example usage: compute_embeddings(wavs, lens) and returns: emb