code: def test_log_prob_broadcast_batch(
      self, is_static, get_distributions, event_shape
  ):
      _, concat_dist = get_distributions(is_static=is_static)
      x_sample = tf.ones([1, 1, 1] + event_shape)
      if tf.executing_eagerly() or is_static:
          with self.assertRaises(Exception):
              concat_dist.log_prob(x_sample)
      else:
          log_prob = concat_dist.log_prob(x_sample)
          with self.assertRaises(Exception):
              self.evaluate(log_prob)
  
imports: from absl.testing import parameterized
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import batch_concat
  from tensorflow_probability.python.distributions import dirichlet
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import onehot_categorical
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 11 for calling the BatchConcatTest obj's underlying member method code with example usage: obj.test_log_prob_broadcast_batch(is_static, get_distributions, event_shape) without return types