code: def masked_std(sample, mask=None):
      """A metric function that computes the standard deviation of each
      sample, excluding padding
  
      Arguments
      ---------
      samples: torch.Tensor
          a tensor of spectrograms
  
      mask: torch.Tensor
          a length mask
  
      Returns
      -------
      result: torch.Tensor
          a tensor fo means
      """
      if mask is None:
          mask = torch.ones_like(sample).bool()
      dims = non_batch_dims(sample)
      mean = unsqueeze_as(masked_mean(sample, mask), sample)
      diff_sq = ((sample - mean) * mask) ** 2
      return (
          diff_sq.sum(dim=dims)
          / (mask.expand_as(diff_sq).sum(dim=dims) - 1)
      ).sqrt()
  
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 30 for calling a global function with example usage: masked_std(sample, mask) and returns: unknown