code: def testBurnin(self):
      if tf.executing_eagerly():
          return
  
      for burnin_dtype in [tf.int8, tf.int16, tf.int32, tf.int64]:
          with self.cached_session():
              var0 = tf.Variable([1.1, 2.1], dtype=tf.float32)
              grads0 = tf.constant([0.1, 0.1], dtype=tf.float32)
              decay_rate = 0.53
              sgd_optimizer = sgld.StochasticGradientLangevinDynamics(
                  3.0,
                  preconditioner_decay_rate=decay_rate,
                  burnin=tf.constant(10, dtype=burnin_dtype),
              )
              sgd_op = sgd_optimizer.apply_gradients([(grads0, var0)])
  
              self.evaluate(tf1.global_variables_initializer())
              # Validate that iterations is initialized to 0.
              self.assertAllCloseAccordingToType(
                  0, self.evaluate(sgd_optimizer.iterations)
              )
              # Run 1 step of sgd
              self.evaluate(sgd_op)
              # Validate that iterations is incremented.
              self.assertAllCloseAccordingToType(
                  1, self.evaluate(sgd_optimizer.iterations)
              )
  
file_path: probability/tensorflow_probability/python/optimizer/sgld_test.py
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.diag_jacobian import diag_jacobian
  from tensorflow_probability.python.optimizer import sgld
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 5 for calling the StochasticGradientLangevinDynamicsOptimizerTest obj's underlying member method code with example usage: obj.testBurnin() without return types