code: def compute_mask_indices_for_one(
      sz,
      mask_prob: float,
      mask_length: int,
      seed=None,
      epoch=None,
      index=None,
      min_masks=0,
  ):
      """
      set seed, epoch, index for deterministic masking
      """
      seed = int(hash((seed, epoch, index)) % 1e6) if seed else None
      rng = np.random.default_rng(seed)
  
      # decide elements to mask
      mask = np.full(sz, False)
      num_mask = int(
          # add a random number for probabilistic rounding
          mask_prob * sz / float(mask_length)
          + rng.random()
      )
      num_mask = max(min_masks, num_mask)
  
      # multiple masking as described in the vq-wav2vec paper (https://arxiv.org/abs/1910.05453)
      mask_idc = rng.choice(sz, num_mask, replace=False)
      mask_idc = np.concatenate(
          [mask_idc + i for i in range(mask_length)]
      )
      mask_idc = mask_idc[mask_idc < len(mask)]
      try:
          mask[mask_idc] = True
      except:  # something wrong
          print(
              f"Assigning mask indexes {mask_idc} to mask {mask} failed!"
          )
          raise
  
      return mask
  
imports: import contextlib
  import itertools
  import logging
  import re
  import warnings
  from typing import Optional, Tuple
  import math
  import numpy
  import torch
  from fairseq.file_io import PathManager
  from fairseq import utils
  import os
label_desc: Use image node_id 21 for calling a global function with example usage: compute_mask_indices_for_one(sz, mask_prob, mask_length, seed, epoch, index, min_masks) and returns: mask