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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 transformer_base_vq_ada_32ex_packed():
 """Set of hyperparameters for lm1b packed following tpu params."""

  hparams = transformer_base_v2()
  expert_utils.update_hparams_for_vq_gating(hparams)
  hparams.moe_num_experts = 32
  hparams.gating_type = "vq"
  # this gives us a batch size of 16 because each seq is len 256
  hparams.batch_size = 5072
  hparams.ffn_layer = "local_moe"
  hparams.shared_embedding_and_softmax_weights = False
  hparams.learning_rate_warmup_steps = 10000
  # one epoch for languagemodel_lm1b32k_packed = 27200 steps w/ bsize 128
  hparams.learning_rate_decay_steps = 27200
  hparams.num_heads = 4
  hparams.num_blocks = 1
  hparams.moe_k = 1
  hparams.num_decoder_layers = 6
  hparams.label_smoothing = 0.
  hparams.layer_prepostprocess_dropout = 0.1
  hparams.layer_postprocess_sequence = "dan"
  hparams.layer_preprocess_sequence = "none"
  hparams.weight_decay = 1e-06
  hparams.attention_dropout = 0.1
  hparams.optimizer = "Adafactor"
  hparams.learning_rate_schedule = "linear_warmup*rsqrt_decay*linear_decay"
  hparams.activation_dtype = "float32"
  hparams.learning_rate = 0.1
  hparams.learning_rate_constant = 1.0
  return hparams