code: def compute_embeddings(wavs, wav_lens):
      """Compute speaker embeddings.
  
      Arguments
      ---------
      wavs : Torch.Tensor
          Tensor containing the speech waveform (batch, time).
          Make sure the sample rate is fs=16000 Hz.
      wav_lens: Torch.Tensor
          Tensor containing the relative length for each sentence
          in the length (e.g., [0.8 0.6 1.0])
      """
      wavs = wavs.to(run_opts["device"])
      wav_lens = wav_lens.to(run_opts["device"])
      with torch.no_grad():
          feats = params["compute_features"](wavs)
          feats = params["mean_var_norm"](feats, wav_lens)
          embeddings = params["embedding_model"](feats, wav_lens)
      return embeddings.squeeze(1)
  
imports: import os
  import sys
  import torch
  import torchaudio
  import logging
  import speechbrain
  import numpy
  import pickle
  from tqdm.contrib import tqdm
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.metric_stats import EER, minDCF
  from speechbrain.processing.PLDA_LDA import StatObject_SB
  from speechbrain.processing.PLDA_LDA import Ndx
  from speechbrain.processing.PLDA_LDA import fast_PLDA_scoring
  from speechbrain.utils.data_utils import download_file
  from speechbrain.utils.distributed import run_on_main
label_desc: Use image node_id 1 for calling a global function with example usage: compute_embeddings(wavs, wav_lens) and returns: embeddings