code: def test_compute_mask_indices_low_prob(self):
      # with these settings num_masked_spans=0.5, which means probabilistic rounding
      # ensures that in 5 out of 10 method calls, num_masked_spans=0, and in
      # the other 5 out of 10, cases num_masked_spans=1
      n_trials = 100
      batch_size = 4
      sequence_length = 100
      mask_prob = 0.05
      mask_length = 10
  
      count_dimensions_masked = 0
      count_dimensions_not_masked = 0
  
      for _ in range(n_trials):
          mask = _compute_mask_indices(
              (batch_size, sequence_length), mask_prob, mask_length
          )
          mask = torch.from_numpy(mask).to(torch_device)
  
          num_masks = torch.sum(mask).item()
  
          if num_masks > 0:
              count_dimensions_masked += 1
          else:
              count_dimensions_not_masked += 1
  
      # as we test for at least 10 masked dimension and at least
      # 10 non-masked dimension, this test could fail with probability:
      # P(100 coin flips, at most 9 heads) = 1.66e-18
      self.assertGreater(count_dimensions_masked, int(n_trials * 0.1))
      self.assertGreater(
          count_dimensions_not_masked, int(n_trials * 0.1)
      )
  
imports: import math
  import unittest
  import numpy
  from datasets import load_dataset
  from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
  from transformers import Data2VecAudioConfig, is_torch_available
  from transformers.testing_utils import is_pt_flax_cross_test, require_soundfile, require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init
label_desc: Use image node_id 2 for calling the Data2VecAudioUtilsTest obj's underlying member method code with example usage: obj.test_compute_mask_indices_low_prob() without return types