code: def _stochastic_process_specific_hp_settings(test_method):
      return tfp_hps.tfp_hp_settings(
          default_max_examples=5,
          suppress_health_check=[
              hp.HealthCheck.too_slow,
              hp.HealthCheck.filter_too_much,
              hp.HealthCheck.data_too_large,
          ],
      )(test_method)
  
imports: from absl.testing import parameterized
  import hypothesis
  from hypothesis import strategies
  import numpy
  import six
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gaussian_process
  from tensorflow_probability.python.distributions import gaussian_process_regression_model
  from tensorflow_probability.python.distributions import student_t_process
  from tensorflow_probability.python.distributions import student_t_process_regression_model
  from tensorflow_probability.python.distributions import variational_gaussian_process
  from tensorflow_probability.python.experimental.distributions import marginal_fns
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.psd_kernels import hypothesis_testlib
label_desc: Use image node_id 1 for calling a global function with example usage: _stochastic_process_specific_hp_settings(test_method) and returns: tfp_hps