code: def testVaryingBatchShapeErrorDynamicDims(self):
      if tf.executing_eagerly():
          return
      with self.assertRaisesOpError(
          "Distributions must have the same `batch_shape`"
      ):
          loc1 = tf1.placeholder_with_default(tf.zeros([1]), shape=None)
          loc2 = tf1.placeholder_with_default(tf.zeros([2]), shape=None)
          dist = blockwise.Blockwise(
              [
                  normal.Normal(loc1, tf.ones_like(loc1)),
                  normal.Normal(loc2, tf.ones_like(loc2)),
              ],
              validate_args=True,
          )
          self.evaluate(dist.mean())
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import blockwise
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_coroutine
  from tensorflow_probability.python.distributions import joint_distribution_named
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import mvn_linear_operator
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 6 for calling the BlockwiseTest obj's underlying member method code with example usage: obj.testVaryingBatchShapeErrorDynamicDims() without return types