code: def test_raises_error_on_inconsistent_prior_shapes(self):
      with self.assertRaisesRegexp(
          ValueError,
          "The specified prior does not generate consistent shapes",
      ):
          iterated_filter_lib.IteratedFilter(
              parameter_prior=normal.Normal(0.0, 1.0),
              # Pass an invalid prior fn that ignores parameters and always
              # has batch shape `[]`.
              parameterized_initial_state_prior_fn=lambda _: normal.Normal(
                  0.0, 1.0
              ),
              parameterized_transition_fn=lambda p: normal.Normal(
                  p, 1.0
              ),
              parameterized_observation_fn=lambda s: normal.Normal(
                  s, 1.0
              ),
          )
  
file_path: probability/tensorflow_probability/python/experimental/sequential/iterated_filter_test.py
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import deterministic
  from tensorflow_probability.python.distributions import joint_distribution_named
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.experimental.sequential import iterated_filter
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 3 for calling the IteratedFilterTest obj's underlying member method code with example usage: obj.test_raises_error_on_inconsistent_prior_shapes() without return types