code: def length_range(feats, len_dim):
      """Creates a tensor with a range in a single dimension to one matching the shape
      of a its tensor
  
      Arguments
      ---------
      feats: torch.Tensor
          a features tensor of arbitrary shape
      len_dim: torch.Tensor
          the dimension used as length
  
      Returns
      -------
      result: torch.Tensor
          a tensor matching the shape of feats with an 0 to max-length range along
          the length dimension repeated across other dimensions"""
      max_len = feats.size(len_dim)
      feats_range = torch.arange(max_len).to(feats.device)
      out = unsqueeze_1d(feats_range, feats.dim(), len_dim)
      repeat_dim = [
          feats_size // out_size
          for feats_size, out_size in zip(feats.shape, out.shape)
      ]
      return out.repeat(*repeat_dim)
  
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 27 for calling a global function with example usage: length_range(feats, len_dim) and returns: out