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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def diet_adam_optimizer_params():
 """Default hyperparameters for a DietAdamOptimizer. Returns: a hyperparameters object. """

  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
  )