code: def attention_lm_base():
    
    hparams = common_hparams.basic_params1()
    hparams.hidden_size = 1024
    hparams.batch_size = 8192
    hparams.max_length = 256
    hparams.dropout = 0.0
    hparams.clip_grad_norm = 0.  # i.e. no gradient clipping
    hparams.optimizer_adam_epsilon = 1e-9
    hparams.learning_rate_decay_scheme = "noam"
    hparams.learning_rate = 0.1
    hparams.learning_rate_warmup_steps = 2000
    hparams.initializer_gain = 1.0
    hparams.num_hidden_layers = 6
    hparams.initializer = "uniform_unit_scaling"
    hparams.weight_decay = 0.0
    hparams.optimizer_adam_beta1 = 0.9
    hparams.optimizer_adam_beta2 = 0.98
    hparams.label_smoothing = 0.0
    hparams.shared_embedding_and_softmax_weights = False
  
    hparams.add_hparam("filter_size", 4096)  # Add new ones like this.
    # attention-related flags
    hparams.add_hparam("num_heads", 8)
    hparams.add_hparam("attention_key_channels", 0)
    hparams.add_hparam("attention_value_channels", 0)
    # All hyperparameters ending in "dropout" are automatically set to 0.0
    # when not in training mode.
    hparams.add_hparam("attention_dropout", 0.0)
    hparams.add_hparam("relu_dropout", 0.0)
    hparams.add_hparam("pos", "timing")  # timing, none
    hparams.add_hparam("encoder_full_attention", False)
    return hparams
docs: Set of hyperparameters.