code: def test_chain_batch_shape(self, shape_in, expected_batch_shape_out):
      self.skipTest("b/275876892")
      batch_shape = [2, 3]
      num_results = 1
      num_timesteps = 5
      observed_time_series = self._build_tensor(
          np.random.randn(*(batch_shape + [num_timesteps]))
      )
      model = self._build_model(observed_time_series)
      samples, _ = fitting.fit_with_hmc(
          model,
          observed_time_series,
          num_results=num_results,
          chain_batch_shape=shape_in,
          num_warmup_steps=1,
          num_variational_steps=1,
      )
  
      self.evaluate(tf1.global_variables_initializer())
      for parameter, parameter_samples in zip(
          model.parameters, samples
      ):
          self.assertAllEqual(
              self._shape_as_list(parameter_samples),
              [num_results]
              + expected_batch_shape_out
              + self._batch_shape_as_list(parameter.prior)
              + self._event_shape_as_list(parameter.prior),
          )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.sts import fitting
  from tensorflow_probability.python.sts.components import local_linear_trend
  from tensorflow_probability.python.sts.components import seasonal
  from tensorflow_probability.python.sts.components import sum
  from tensorflow_probability.python.sts.internal import missing_values_util
  from tensorflow_probability.python.vi import csiszar_divergence
  from tensorflow_probability.python.vi import optimization
label_desc: Use image node_id 1 for calling the HMCTestsStatic32 obj's underlying member method code with example usage: obj.test_chain_batch_shape(shape_in, expected_batch_shape_out) without return types