docstr: Sampling from the Standard Normal Distribution.
docstr_tok: ["Sampling","from","the","Standard","Normal","Distribution","."]
code: def testLangevin1DNormal(self):
      """Sampling from the Standard Normal Distribution."""
      dtype = np.float32
      nchains = 32
  
      target = normal.Normal(loc=dtype(0), scale=dtype(1))
      samples = sample.sample_chain(
          num_results=500,
          current_state=np.ones([nchains], dtype=dtype),
          kernel=langevin.MetropolisAdjustedLangevinAlgorithm(
              target_log_prob_fn=target.log_prob,
              step_size=0.75,
              volatility_fn=lambda *args: 0.5,
          ),
          num_burnin_steps=200,
          trace_fn=None,
          seed=test_util.test_seed(),
      )
  
      sample_mean = tf.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(sample_mean_, 0.0, atol=0.12)
      self.assertAllClose(sample_std_, 1.0, atol=0.1)
  
imports: import numpy
  import tensorflow.compat.v2
  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 langevin
  from tensorflow_probability.python.mcmc import sample
label_desc: Use image node_id 1 for calling the LangevinTest obj's underlying member method code with example usage: obj.testLangevin1DNormal() without return types