code: def _offset_to_tensor(offset, lengths):
      """Converts a variety of offset representations to a component x batch tensor,
      used by concat_padded_features. offset can be a tensor, a list of tensors (where
      each element is a tensor of relative offsets similar to lengths), a list of floats
      (in which case all batch elements are presumed to have the same offset)
  
      Arguments
      ---------
      offset: list|torch.Tensor
          a list or tensor of offsets
  
      lengths: torch.Tensor
          a length tensor
  
      Returns
      -------
      result: torch.Tensor
          a tensor of offsets
      """
      if offset is None:
          result = None
      elif torch.is_tensor(offset):
          result = offset
      elif isinstance(offset, Number):
          result = torch.ones_like(lengths) * offset
      elif isinstance(offset, list):
          if isinstance(offset[0], Number):
              result = (
                  torch.tensor(offset).unsqueeze(-1).to(lengths.device)
              )
          else:
              result = torch.concat(
                  [item.unsqueeze(0) for item in offset]
              )
      else:
          raise ValueError(
              "The offset must be a number, a tensor or a list of tensors"
          )
      return result
  
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 23 for calling a global function with example usage: _offset_to_tensor(offset, lengths) and returns: result