code: def _get_mode_dependent_settings():
      if tf.executing_eagerly() and not JAX_MODE:
          num_results = 100
          tolerance = 0.2
      else:
          num_results = 400
          tolerance = 0.1
      return num_results, tolerance
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import distribute_lib
  from tensorflow_probability.python.internal import distribute_test_lib
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc import random_walk_metropolis
  from tensorflow_probability.python.mcmc import sample
  from tensorflow_probability.python.mcmc import slice_sampler_kernel
  from tensorflow_probability.python.stats import sample_stats
label_desc: Use image node_id 1 for calling a global function with example usage: _get_mode_dependent_settings() and returns: num_results,  tolerance