code: def length_to_mask(length, max_len=None, dtype=None, device=None):
      """Creates a binary mask for each sequence.
  
      Reference: https://discuss.pytorch.org/t/how-to-generate-variable-length-mask/23397/3
  
      Arguments
      ---------
      length : torch.LongTensor
          Containing the length of each sequence in the batch. Must be 1D.
      max_len : int
          Max length for the mask, also the size of the second dimension.
      dtype : torch.dtype, default: None
          The dtype of the generated mask.
      device: torch.device, default: None
          The device to put the mask variable.
  
      Returns
      -------
      mask : tensor
          The binary mask.
  
      Example
      -------
      >>> length=torch.Tensor([1,2,3])
      >>> mask=length_to_mask(length)
      >>> mask
      tensor([[1., 0., 0.],
              [1., 1., 0.],
              [1., 1., 1.]])
      """
      assert len(length.shape) == 1
  
      if max_len is None:
          max_len = (
              length.max().long().item()
          )  # using arange to generate mask
      mask = torch.arange(
          max_len, device=length.device, dtype=length.dtype
      ).expand(len(length), max_len) < length.unsqueeze(1)
  
      if dtype is None:
          dtype = length.dtype
  
      if device is None:
          device = length.device
  
      mask = torch.as_tensor(mask, dtype=dtype, device=device)
      return mask
  
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 16 for calling a global function with example usage: length_to_mask(length, max_len, dtype, device) and returns: mask