      assert history.history[accuracy][-1] >= target
      config = k_optimizers.serialize(optimizer)
      optim = k_optimizers.deserialize(config)
      new_config = k_optimizers.serialize(optim)
      new_config["class_name"] = new_config["class_name"].lower()
      assert config == new_config
  
      # Test constraints.
      if get_backend() == "amd":
 
</s> remove     Example of warmup:
 </s> add     Example of warm up: </s> remove         parans: list
 </s> add         params: list </s> remove     _test_optimizer(optimizers.AdaBelief(), target=0.6)
 </s> add     _test_optimizer(optimizers.AdaBelief(), target=0.5) </s> remove         an optimizer. The same optimizer can be reinstantiated later (without any saved state) from
        this configuration.
 </s> add         an optimizer. The same optimizer can be re-instantiated later (without any saved state)
        from this configuration. </s> remove         an optimizer. The same optimizer can be reinstantiated later (without any saved state) from
        this configuration.
 </s> add         an optimizer. The same optimizer can be re-instantiated later (without any saved state)
        from this configuration.
    config["class_name"] = config["class_name"].lower()
      assert history.history[accuracy][-1] >= target
      config = k_optimizers.serialize(optimizer)
      optim = k_optimizers.deserialize(config)
      new_config = k_optimizers.serialize(optim)
    config["class_name"] = config["class_name"].lower()
      new_config["class_name"] = new_config["class_name"].lower()
      assert config == new_config
  
      # Test constraints.
      if get_backend() == "amd":
keep keep keep add keep keep keep keep keep
 Update Travis Tests for optimizers 
tests/lib/model/optimizers_test.py