code: def test_offset_normal_fails(self):
      n_samples = 500
      # three 4-variate chains taken from Normal(0, 1) that have been
      # shifted. Since every chain is shifted, they are not the same, and the
      # test should fail.
      offset = np.array([1.0, -1.0, 2.0]).reshape(3, 1)
      rng = test_util.test_np_rng()
      offset_samples = rng.randn(n_samples, 3, 4) + offset
      rhat_reducer = psrr.PotentialScaleReductionReducer(
          independent_chain_ndims=1
      )
      rhat = self.evaluate(
          test_fixtures.reduce(rhat_reducer, offset_samples)
      )
      self.assertAllEqual((4,), rhat.shape)
      self.assertAllGreater(rhat, 1.2)
  
file_path: probability/tensorflow_probability/python/experimental/mcmc/potential_scale_reduction_reducer_test.py
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.experimental.mcmc import potential_scale_reduction_reducer
  from tensorflow_probability.python.experimental.mcmc import tracing_reducer
  from tensorflow_probability.python.experimental.mcmc.internal import test_fixtures
  from tensorflow_probability.python.experimental.mcmc.sample_fold import sample_fold
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc import diagnostic
  from tensorflow_probability.python.mcmc import hmc
label_desc: Use image node_id 3 for calling the PotentialScaleReductionReducerTest obj's underlying member method code with example usage: obj.test_offset_normal_fails() without return types