code: def diet_adam_optimizer_params():
    
    return hparam.HParams(
        quantize=True,  # use 16-bit fixed-point
        quantization_scale=10.0 / tf.int16.max,
        optimizer="DietAdam",
        learning_rate=1.0,
        learning_rate_warmup_steps=2000,
        learning_rate_decay_scheme="noam",  # "noam" or "none"
        epsilon=1e-10,
        beta1=0.0,  # we can save memory if beta1=0
        beta2=0.98,
        factored_second_moment_accumulator=True,  # this saves memory
    )
docs: Default hyperparameters for a DietAdamOptimizer.
  
    Returns:
      a hyperparameters object.