code: def concat_padded_features(
      feats, lens, dim=1, feats_slice_start=None, feats_slice_end=None
  ):
      """Concatenates multiple padded feature tensors into a single
      padded tensor in a vectorized manner without including the
      padding in the final tensor, adding padding only at the end.
      The function supports optional relative sicing of the tensors.
  
      One possible use case is to concatenate batches of spectrograms
      or audio.
  
      Arguments
      ---------
      feats: list
          a list of padded tesnors
  
      lens: list
          a list of length tensors
  
      feats_slice_start: list
          offsets, relative to the beginning of the sequence, for each
          of the tensors being concatenated. This is useful if only
          a subsequence of some slices is included
  
      feats_slice_end: list
          offsets, relative to the end of the sequence, for each
          of the tensors being concatenated. This is useful if only
          a subsequence of some slices is included
  
  
  
      Returns
      -------
      out: torch.Tensor
          a concatenated tensor
      """
      first_item = feats[0]
      item_lengths = torch.tensor(
          [item.size(dim) for item in feats]
      ).to(first_item.device)
      lens = torch.concat([len_rel.unsqueeze(0) for len_rel in lens])
      lens_abs = (lens * item_lengths.unsqueeze(-1)).int()
  
      feats_slice_start = _offset_to_tensor(feats_slice_start, lens_abs)
      feats_slice_end = _offset_to_tensor(feats_slice_end, lens_abs)
  
      out_start, out_end = _lens_to_boundaries(
          lens_abs, feats_slice_start, feats_slice_end, cumulative=True
      )
      in_start, in_end = _lens_to_boundaries(
          lens_abs, feats_slice_start, feats_slice_end, cumulative=False
      )
      total_length = out_end.max().int().item()
  
      out_shape = list(first_item.shape)
      out_shape[dim] = total_length
      out = torch.zeros(out_shape).to(first_item.device)
      for (
          item,
          item_in_start,
          item_in_end,
          item_out_start,
          item_out_end,
      ) in zip(feats, in_start, in_end, out_start, out_end):
          in_mask = _boundaries_to_mask(
              item, item_in_start, item_in_end, dim
          )
          out_mask = _boundaries_to_mask(
              out, item_out_start, item_out_end, dim
          )
          out[out_mask] = item[in_mask]
  
      out_lens = out_end[-1, :].float() / total_length
  
      return out, out_lens
  
imports: import math
  import os
  import re
  import csv
  import shutil
  import urllib.request
  import collections.abc
  import torch
  import tqdm
  import pathlib
  import speechbrain
  from numbers import Number
label_desc: Use image node_id 22 for calling a global function with example usage: concat_padded_features(feats, lens, dim, feats_slice_start, feats_slice_end) and returns: out,  out_lens