code: def test_deterministic_results_with_seed(self):
      stochastic_loss_fn = lambda x, seed: (
          x - x
      ) + tf.random.stateless_normal([], seed=seed)
      opt = StatelessSGD(1e-3)
      seed = test_util.test_seed(sampler_type="stateless")
      _, losses1 = self.evaluate(
          minimize_stateless(
              loss_fn=stochastic_loss_fn,
              init=0.0,
              num_steps=10,
              optimizer=opt,
              seed=seed,
          )
      )
      _, losses2 = self.evaluate(
          minimize_stateless(
              loss_fn=stochastic_loss_fn,
              init=0.0,
              num_steps=10,
              optimizer=opt,
              seed=seed,
          )
      )
      self.assertAllEqual(losses1, losses2)
      # Make sure we got different samples at each step.
      self.assertAllGreater(tf.abs(losses1[1:] - losses1[:-1]), 1e-4)
  
imports: import collections
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python import optimizer
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.math.minimize import minimize
  from tensorflow_probability.python.math.minimize import minimize_stateless
label_desc: Use image node_id 12 for calling the MinimizeStatelessTests obj's underlying member method code with example usage: obj.test_deterministic_results_with_seed() without return types