code: def test_systematic_resampler_large(self):
      if self.use_xla and tf.executing_eagerly():
          self.skipTest(
              "No need to test XLA under all execution regimes."
          )
  
      num_probs = 10000
      log_probs = tf.fill(
          [num_probs], -tf.math.log(self.dtype(num_probs))
      )
      self.evaluate(
          self.maybe_compiler(resample_systematic)(
              log_probs,
              num_probs,
              [],
              seed=test_util.test_seed_stream(),
          )
      )
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import chi2
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.experimental.mcmc.weighted_resampling import _resample_using_log_points
  from tensorflow_probability.python.experimental.mcmc.weighted_resampling import _scatter_nd_batch
  from tensorflow_probability.python.experimental.mcmc.weighted_resampling import resample
  from tensorflow_probability.python.experimental.mcmc.weighted_resampling import resample_deterministic_minimum_error
  from tensorflow_probability.python.experimental.mcmc.weighted_resampling import resample_independent
  from tensorflow_probability.python.experimental.mcmc.weighted_resampling import resample_stratified
  from tensorflow_probability.python.experimental.mcmc.weighted_resampling import resample_systematic
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 5 for calling the _SMCResamplersTest obj's underlying member method code with example usage: obj.test_systematic_resampler_large() without return types