      >>> config = tf.keras.optimizers.serialize(optimizer)
      >>> new_optimizer = tf.keras.optimizers.deserialize(config,
      ...                                                 custom_objects=dict(AdaBelief=AdaBelief))
  
      Example of warmup:
  
      >>> opt = AdaBelief(lr=1e-3, total_steps=10000, warmup_proportion=0.1, min_lr=1e-5)
  
      In the above example, the learning rate will increase linearly from 0 to `lr` in 1000 steps,
      then decrease linearly from `lr` to `min_lr` in 9000 steps.
 
</s> remove         parans: list
 </s> add         params: list </s> remove         the iterations count of the optimizer, followed by the optimizer's state variables in the
 </s> add         the iterations count of the optimizer, followed by the optimizers state variables in the </s> remove             List of tf variables to create slots for
 </s> add             List of tensorflow variables to create slots for </s> remove         The weights of an optimizer are its state (ie, variables). This function takes the weight
 </s> add         The weights of an optimizer are its state (IE, variables). This function takes the weight
    Example of warm up:
      >>> config = tf.keras.optimizers.serialize(optimizer)
      >>> new_optimizer = tf.keras.optimizers.deserialize(config,
      ...                                                 custom_objects=dict(AdaBelief=AdaBelief))
  
    Example of warm up:
  
      >>> opt = AdaBelief(lr=1e-3, total_steps=10000, warmup_proportion=0.1, min_lr=1e-5)
  
      In the above example, the learning rate will increase linearly from 0 to `lr` in 1000 steps,
      then decrease linearly from `lr` to `min_lr` in 9000 steps.
keep keep keep keep replace keep keep keep keep keep
 Update Travis Tests for optimizers 
lib/model/optimizers_tf.py