code: def test_skip_gram_sample_random_skips_default_seed():
      """Tests outputs are still random when no op-level seed is
      specified."""
  
      # This is needed since tests set a graph-level seed by default. We want
      # to explicitly avoid setting both graph-level seed and op-level seed,
      # to simulate behavior under non-test settings when the user doesn't
      # provide a seed to us. This results in random_seed.get_seed() returning
      # None for both seeds, forcing the C++ kernel to execute its default
      # seed logic.
      tf.random.set_seed(None)
  
      # Uses an input tensor with 10 words, with possible skip ranges in
      # [1, 5]. Thus, the probability that two random samplings would result
      # in the same outputs is 1/5^10 ~ 1e-7 (aka the probability of this test
      # being flaky).
      input_tensor = tf.constant([str(x) for x in range(10)])
  
      # Do not provide an op-level seed here!
      tokens_1, labels_1 = text.skip_gram_sample(
          input_tensor, min_skips=1, max_skips=5
      )
      tokens_2, labels_2 = text.skip_gram_sample(
          input_tensor, min_skips=1, max_skips=5
      )
  
      if len(tokens_1) == len(tokens_2):
          assert list(tokens_1) != list(tokens_2)
      if len(labels_1) == len(labels_2):
          assert list(labels_1) != list(labels_2)
  
imports: import csv
  import os
  import tempfile
  import numpy
  import pytest
  import tensorflow
  from tensorflow_addons import text
  from tensorflow_addons.text import skip_gram_ops
label_desc: Use image node_id 9 for calling a global function with example usage: test_skip_gram_sample_random_skips_default_seed() without return types