code: def _vad_feature_segment_collate_fn(
      batch,
      window_length_in_sec,
      shift_length_in_sec,
      frame_unit_in_sec,
  ):
      """collate batch of audio features, features len, tokens, tokens len
      Args:
          batch (Optional[FloatTensor], Optional[LongTensor], LongTensor,
              LongTensor):  A tuple of tuples of signal, signal lengths,
              encoded tokens, and encoded tokens length.  This collate func
              assumes the signals are 1d torch tensors (i.e. mono audio).
              batch size equals to 1.
      """
      slice_length = int(window_length_in_sec / frame_unit_in_sec)
      audio_features, feat_lengths, _, tokens_lengths = zip(*batch)
  
      slice_length = int(min(slice_length, max(feat_lengths)))
      shift = int(shift_length_in_sec / frame_unit_in_sec)
      has_audio = feat_lengths[0] is not None
  
      f_dim = audio_features[0].shape[0]
      audio_features, num_slices, tokens, feat_lengths = [], [], [], []
      append_len_start = torch.div(
          slice_length, 2, rounding_mode="trunc"
      )
      append_len_end = slice_length - torch.div(
          slice_length, 2, rounding_mode="trunc"
      )
      for feat_i, feat_i_len, tokens_i, _ in batch:
          start = torch.zeros(f_dim, append_len_start)
          end = torch.zeros(f_dim, append_len_end)
          feat_i = torch.cat((start, feat_i, end), dim=1)
          feat_i_len += slice_length
  
          if has_audio:
              slices = max(
                  1,
                  torch.div(
                      feat_i_len - slice_length,
                      shift,
                      rounding_mode="trunc",
                  ),
              )
  
              for slice_id in range(slices):
                  start_idx = slice_id * shift
                  end_idx = start_idx + slice_length
                  feat_slice = feat_i[:, start_idx:end_idx]
                  audio_features.append(feat_slice)
  
              num_slices.append(slices)
              tokens.extend([tokens_i] * slices)
              feat_lengths.extend([slice_length] * slices)
  
      if has_audio:
          audio_features = torch.stack(audio_features)
          feat_lengths = torch.tensor(feat_lengths)
      else:
          audio_features, feat_lengths = None, None
  
      tokens = torch.stack(tokens)
      tokens_lengths = torch.tensor(num_slices)
      return audio_features, feat_lengths, tokens, tokens_lengths
  
imports: from typing import Dict, List, Optional
  import torch
  from nemo.collections.asr.parts.preprocessing.feature_loader import ExternalFeatureLoader
  from nemo.collections.common.parts.preprocessing import collections
  from nemo.core.classes import Dataset
  from nemo.core.neural_types import AcousticEncodedRepresentation, LabelsType, LengthsType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 3 for calling a global function with example usage: _vad_feature_segment_collate_fn(batch, window_length_in_sec, shift_length_in_sec, frame_unit_in_sec) and returns: audio_features,  feat_lengths,  tokens,  tokens_lengths