code: def testBrokenShapesDynamic(self):
      d0_param = tf.Variable([2.0, 3], shape=tf.TensorShape(None))
      d1_param = tf.Variable([1.0], shape=tf.TensorShape(None))
  
      self.evaluate([d0_param.initializer, d1_param.initializer])
  
      with self.assertRaisesWithPredicateMatch(
          tf.errors.InvalidArgumentError, "batch shape must match cat"
      ):
          d = mixture.Mixture(
              categorical.Categorical([0.1, 0.2]),
              [
                  normal.Normal(loc=d0_param, scale=d0_param),
                  normal.Normal(loc=d1_param, scale=d1_param),
              ],
              validate_args=True,
          )
          self.evaluate([d.sample(seed=test_util.test_seed())])
  
imports: import contextlib
  import warnings
  import numpy
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import dirichlet
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import half_normal
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import mixture
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 4 for calling the MixtureTest obj's underlying member method code with example usage: obj.testBrokenShapesDynamic() without return types