code: def test_random_spans_noise_mask(self):
      length = 100
      noise_density = 0.15
      mean_noise_span_length = 3.0
  
      # Take 5 random seed1, seed2 values.
      for seed in np.random.randint(0, 100, (5, 2)):
          is_noise = data.random_spans_noise_mask(
              length,
              noise_density,
              mean_noise_span_length,
              seed1=seed[0],
              seed2=seed[1],
          )
          is_noise = is_noise.astype(np.int32)
          # noise_density fraction of tokens are produced
          self.assertEqual(np.sum(is_noise), noise_density * length)
          # Get span lengths and make sure the average is what we expect.
          actual_span_lengths = self._get_span_lengths(is_noise)
          average_span_length = sum(actual_span_lengths) / len(
              actual_span_lengths
          )
          self.assertEqual(mean_noise_span_length, average_span_length)
  
imports: import itertools
  import os
  from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  from trax import data
label_desc: Use image node_id 35 for calling the InputsTest obj's underlying member method code with example usage: obj.test_random_spans_noise_mask() without return types