code: def add_standard_attention_hparams(hparams):
    
    # All hyperparameters ending in "dropout" are automatically set to 0.0
    # when not in training mode.
  
    # hparams used and which should have been defined outside (in
    # common_hparams):
    # Global flags
    # hparams.mode
    # hparams.hidden_size
    # Pre-post processing flags
    # hparams.layer_preprocess_sequence
    # hparams.layer_postprocess_sequence
    # hparams.layer_prepostprocess_dropout
    # hparams.norm_type
    # hparams.norm_epsilon
    # Mixture-of-Expert flags
    # hparams.moe_hidden_sizes
    # hparams.moe_num_experts
    # hparams.moe_k
    # hparams.moe_loss_coef
  
    # Attention layers flags
    hparams.add_hparam("num_heads", 8)
    hparams.add_hparam("attention_key_channels", 0)
    hparams.add_hparam("attention_value_channels", 0)
    hparams.add_hparam("attention_dropout", 0.0)
    # Attention: Local
    hparams.add_hparam("attention_loc_block_length", 256)
    # Attention: Local (unmasked only): How much to look left.
    hparams.add_hparam("attention_loc_block_width", 128)
    # Attention: Memory-compressed
    hparams.add_hparam("attention_red_factor", 3)
    hparams.add_hparam("attention_red_type", "conv")
    hparams.add_hparam("attention_red_nonlinearity", "none")
  
    # Fully connected layers flags
    # To be more consistent, should use filter_size to also control the MOE
    # size if moe_hidden_sizes not set.
    hparams.add_hparam("filter_size", 2048)
    hparams.add_hparam("relu_dropout", 0.0)
  
    return hparams
docs: Adds the hparams used by get_standardized_layers.