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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 set_default_moe_hparams(hparams):
 """Add necessary hyperparameters for mixture-of-experts."""

  hparams.moe_num_experts = 16
  hparams.moe_loss_coef = 1e-2
  hparams.add_hparam("moe_gating", "top_2")
  # Experts have fixed capacity per batch.  We need some extra capacity
  # in case gating is not perfectly balanced.
  # moe_capacity_factor_* should be set to a value >=1.
  hparams.add_hparam("moe_capacity_factor_train", 1.25)
  hparams.add_hparam("moe_capacity_factor_eval", 2.0)
  hparams.add_hparam("moe_capacity_factor_second_level", 1.0)
  # Each expert has a hidden layer with this size.
  hparams.add_hparam("moe_hidden_size", 4096)
  # For gating, divide inputs into groups of this size before gating.
  # Each group sends the same number of inputs to each expert.
  # Ideally, the group size would be the whole batch, but this is expensive
  # due to our use of matrix multiplication for reordering.
  hparams.add_hparam("moe_group_size", 1024)
  # For top_2 gating, whether to impose an additional loss in order to make
  # the experts equally used as the second-place expert.
  hparams.add_hparam("moe_use_second_place_loss", 0)
  # In top_2 gating, policy for whether to use a second-place expert.
  # Legal values are:
  #    "all": always
  #    "none": never
  #    "threshold": if gate value > the given threshold
  #    "random": if gate value > threshold*random_uniform(0,1)
  hparams.add_hparam("moe_second_policy_train", "random")
  hparams.add_hparam("moe_second_policy_eval", "random")
  hparams.add_hparam("moe_second_threshold_train", 0.2)
  hparams.add_hparam("moe_second_threshold_eval", 0.2)