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 is an in-place operation
  
      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])
      >>> clean_padding_(x, length=length, mask_value=10.)
      >>> x
      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]]])
      >>> clean_padding_(x, length=length, mask_value=10., len_dim=2)
      >>> x
      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]]])
      """
      max_len = tensor.size(len_dim)
      mask = length_to_mask(length * max_len, max_len).bool()
      mask_unsq = mask[(...,) + (None,) * (tensor.dim() - 2)]
      mask_t = mask_unsq.transpose(1, len_dim).expand_as(tensor)
      tensor[~mask_t] = mask_value
  
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 27 for calling a global function with example usage: clean_padding_(tensor, length, len_dim, mask_value) without return types