code: def test_variable_sample_shape_exception(self):
      loc = tf.Variable(tf.zeros([4, 5, 3]), shape=tf.TensorShape(None))
      scale = tf.Variable(tf.ones([]), shape=tf.TensorShape(None))
      sample_shape = tf.Variable([[1, 2]], shape=tf.TensorShape(None))
      with self.assertRaisesWithPredicateMatch(
          Exception,
          "Argument `sample_shape` must be either a scalar or a vector.",
      ):
          dist = sample.Sample(
              independent.Independent(
                  logistic.Logistic(loc=loc, scale=scale),
                  reinterpreted_batch_ndims=1,
              ),
              sample_shape=sample_shape,
              validate_args=True,
          )
          self.evaluate(
              [v.initializer for v in dist.trainable_variables]
          )
          self.evaluate(dist.mean())
  
imports: import functools
  import os
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import scale_matvec_tril
  from tensorflow_probability.python.distributions import cholesky_lkj
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import log_prob_ratio
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 12 for calling the SampleDistributionTest obj's underlying member method code with example usage: obj.test_variable_sample_shape_exception() without return types