code: def _boundaries_to_mask(target, start, end, len_dim=1):
      """For a given features tensor and tensors of start and end indexes,
      computes the corresponding Boolean mask
  
      Arguments
      ---------
      target: torch.Tensor
          the target tensor
  
      start: torch.Tensor
          the tensor indicating the starting positions along the length
          dimension within each batch
      end: torch.Tensor
          the tensor indicating the final positions within each batch
  
      len_dim: int
          the dimension used as the length
  
      Returns
      -------
      mask: torch.Tensor
          a Boolean mask of the same shape as target"""
      out_range = length_range(target, len_dim)
      feats_dim = target.dim()
      item_start = unsqueeze_1d(start, feats_dim, 0)
      item_end = unsqueeze_1d(end, feats_dim, 0)
      mask = (item_start <= out_range) & (out_range < item_end)
      return mask
  
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 25 for calling a global function with example usage: _boundaries_to_mask(target, start, end, len_dim) and returns: mask