code: def pad_divisible(
      tensor, length=None, factor=2, len_dim=1, pad_value=0
  ):
      """Adds extra padding to the specified dimension of a tensor to make
      it divisible  by the specified factor. This is useful when passing
      variable-length sequences to downsampling UNets or other similar
      architectures in which inputs are expected to be divisible by the
      downsampling factor
  
      Arguments
      ---------
      tensor: torch.Tensor
          the tensor to be padded, of arbitrary dimension
  
      length: torch.Tensor
          a 1-D tensor of relative lengths
  
      factor: int
          the divisibility factor
  
      len_dim: int
          the index of the dimension used as the length
  
      pad_value: int
          the value with which outputs will be padded
  
      Returns
      -------
      tensor_padded: torch.Tensor
          the tensor, with additional padding if required
  
      length: torch.Tensor
          the adjsted length tensor, if provided
  
      Example
      -------
      >>> x = torch.tensor([[1, 2, 3, 4],
      ...                   [5, 6, 0, 0]])
      >>> lens = torch.tensor([1., .5])
      >>> x_pad, lens_pad = pad_divisible(x, length=lens, factor=5)
      >>> x_pad
      tensor([[1, 2, 3, 4, 0],
              [5, 6, 0, 0, 0]])
      >>> lens_pad
      tensor([0.8000, 0.4000])
      """
  
      time_dim = tensor.size(len_dim)
  
      desired_time_dim = time_dim
      gap = time_dim % factor
      if gap > 0:
          desired_time_dim += factor - gap
  
      new_shape = list(tensor.shape)
      new_shape[len_dim] = desired_time_dim
  
      tensor_padded, _ = pad_right_to(
          tensor,
          new_shape,
          value=pad_value,
      )
  
      # Adjust lengths to the new dimenson, post-padding
      if length is not None:
          length = length * (time_dim / desired_time_dim)
  
      return tensor_padded, length
  
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 17 for calling a global function with example usage: pad_divisible(tensor, length, factor, len_dim, pad_value) and returns: tensor_padded,  length