code: def maybe_compiler(self, f):
      if self.use_xla:
          return tf.function(f, autograph=False, jit_compile=True)
      return f
  
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 12 for calling the _SMCResamplersTest obj's underlying member method code with example usage: obj.maybe_compiler(f) and returns: f, tf