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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 mtf_bitransformer_base():
 """Machine translation base configuration."""

  hparams = mtf_transformer2_base()
  hparams.max_length = 256
  hparams.shared_embedding = True
  # HYPERPARAMETERS FOR THE LAYER STACKS
  hparams.add_hparam("encoder_layers", ["self_att", "drd"] * 6)
  hparams.add_hparam("decoder_layers", ["self_att", "enc_att", "drd"] * 6)
  hparams.add_hparam("encoder_num_layers", 6)
  hparams.add_hparam("decoder_num_layers", 6)
  # number of heads in multihead attention
  hparams.add_hparam("encoder_num_heads", 8)
  hparams.add_hparam("decoder_num_heads", 8)
  hparams.add_hparam("local_attention_radius", 128)

  # default of 0 for standard transformer behavior
  # 1 means a single set of keys and values that are read by all query heads
  hparams.add_hparam("encoder_num_memory_heads", 0)
  hparams.add_hparam("decoder_num_memory_heads", 0)
  # share attention keys and values
  hparams.add_hparam("encoder_shared_kv", False)
  hparams.add_hparam("decoder_shared_kv", False)

  # Parameters for computing the maximum decode length in beam search.
  # Maximum decode length is:
  #    min(max_length,
  #        decode_length_multiplier * input_length + decode_length_constant)
  hparams.add_hparam("decode_length_multiplier", 1.5)
  hparams.add_hparam("decode_length_constant", 10.0)
  # used during decoding
  hparams.add_hparam("alpha", 0.6)
  hparams.sampling_temp = 0.0
  return hparams