code: def attention_lm_base():
      """Set of hyperparameters."""
      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 = 0.0  # 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
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from six.moves import range
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
label_desc: Use image node_id 3 for calling a global function with example usage: attention_lm_base() and returns: hparams