code: def clean_padding(tensor, length, len_dim=1, mask_value=0.0):
      """Sets the value of any padding on the specified tensor to mask_value.
  
      For instance, this can be used to zero out the outputs of an autoencoder
      during training past the specified length.
  
      This version of the operation does not modify the original tensor
  
      Arguments
      ---------
      tensor: torch.Tensor
          a tensor of arbitrary dimension
      length: torch.Tensor
          a 1-D tensor of lengths
      len_dim: int
          the dimension representing the length
      mask_value: mixed
          the value to be assigned to padding positions
  
      Example
      -------
      >>> import torch
      >>> x = torch.arange(5).unsqueeze(0).repeat(3, 1)
      >>> x = x + torch.arange(3).unsqueeze(-1)
      >>> x
      tensor([[0, 1, 2, 3, 4],
              [1, 2, 3, 4, 5],
              [2, 3, 4, 5, 6]])
      >>> length = torch.tensor([0.4, 1.0, 0.6])
      >>> x_p = clean_padding(x, length=length, mask_value=10.)
      >>> x_p
      tensor([[ 0,  1, 10, 10, 10],
              [ 1,  2,  3,  4,  5],
              [ 2,  3,  4, 10, 10]])
      >>> x = torch.arange(5)[None, :, None].repeat(3, 1, 2)
      >>> x = x + torch.arange(3)[:, None, None]
      >>> x = x * torch.arange(1, 3)[None, None, :]
      >>> x = x.transpose(1, 2)
      >>> x
      tensor([[[ 0,  1,  2,  3,  4],
               [ 0,  2,  4,  6,  8]],
      <BLANKLINE>
              [[ 1,  2,  3,  4,  5],
               [ 2,  4,  6,  8, 10]],
      <BLANKLINE>
              [[ 2,  3,  4,  5,  6],
               [ 4,  6,  8, 10, 12]]])
      >>> x_p = clean_padding(x, length=length, mask_value=10., len_dim=2)
      >>> x_p
      tensor([[[ 0,  1, 10, 10, 10],
               [ 0,  2, 10, 10, 10]],
      <BLANKLINE>
              [[ 1,  2,  3,  4,  5],
               [ 2,  4,  6,  8, 10]],
      <BLANKLINE>
              [[ 2,  3,  4, 10, 10],
               [ 4,  6,  8, 10, 10]]])
      """
  
      result = tensor.clone()
      clean_padding_(result, length, len_dim, mask_value)
      return result
  
imports: import os
  import torch
  import logging
  import numpy
  import pickle
  import hashlib
  import csv
  import time
  import torchaudio
  import json
  import re
  from speechbrain.utils.torch_audio_backend import check_torchaudio_backend
label_desc: Use image node_id 28 for calling a global function with example usage: clean_padding(tensor, length, len_dim, mask_value) and returns: result