docstr: Sampling from the Standard Normal Distribution with adaptation.
docstr_tok: ["Sampling","from","the","Standard","Normal","Distribution","with","adaptation","."]
code: def testRWM1DNormal(self):
      """Sampling from the Standard Normal Distribution with adaptation."""
      dtype = np.float32
  
      target = normal.Normal(loc=dtype(0), scale=dtype(1))
      samples = sample.sample_chain(
          num_results=500,
          current_state=dtype([1] * 8),  # 8 parallel chains
          kernel=random_walk_metropolis.RandomWalkMetropolis(
              target.log_prob
          ),
          num_burnin_steps=500,
          trace_fn=None,
          seed=test_util.test_seed(),
      )
  
      sample_mean = tf.math.reduce_mean(samples, axis=(0, 1))
      sample_std = tf.math.reduce_std(samples, axis=(0, 1))
  
      [sample_mean_, sample_std_] = self.evaluate(
          [sample_mean, sample_std]
      )
  
      self.assertAllClose(0.0, sample_mean_, atol=0.2, rtol=0.0)
      self.assertAllClose(1.0, sample_std_, atol=0.2, rtol=0.0)
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import cauchy
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import normal
  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.mcmc import random_walk_metropolis
  from tensorflow_probability.python.mcmc import sample
label_desc: Use image node_id 2 for calling the RWMTest obj's underlying member method code with example usage: obj.testRWM1DNormal() without return types