code: def testDynamicShape(self, nsample, batch_size, nd, dynamic_shape):
      dtype = np.float32
  
      kernel = nuts.NoUTurnSampler(
          target_log_prob_fn=independent.Independent(
              normal.Normal(tf.zeros(nd, dtype=dtype), 1.0), 1
          ).log_prob,
          step_size=0.1,
      )
      x_ = np.zeros([batch_size, nd], dtype=dtype)
      x = tf1.placeholder_with_default(x_, shape=dynamic_shape)
      mcmc_trace_ = sample.sample_chain(
          num_results=nsample,
          current_state=x,
          kernel=kernel,
          trace_fn=None,
          seed=test_util.test_seed(),
      )
      mcmc_trace = self.evaluate(mcmc_trace_)
      self.assertAllEqual(mcmc_trace.shape, [nsample, batch_size, nd])
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import half_normal
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_coroutine
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  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.distributions import sample
  from tensorflow_probability.python.distributions import sigmoid_beta
  from tensorflow_probability.python.distributions import student_t
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import distribute_lib
  from tensorflow_probability.python.internal import distribute_test_lib
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import generic
  from tensorflow_probability.python.mcmc import diagnostic
  from tensorflow_probability.python.mcmc import dual_averaging_step_size_adaptation
  from tensorflow_probability.python.mcmc import nuts
  from tensorflow_probability.python.mcmc import sample
  from tensorflow_probability.python.mcmc import transformed_kernel
  from tensorflow_probability.python.stats import sample_stats
label_desc: Use image node_id 11 for calling the NutsTest obj's underlying member method code with example usage: obj.testDynamicShape(nsample, batch_size, nd, dynamic_shape) without return types