text: """Configurations of Transformer model
  """
  import copy
  import texar as tx
  
  random_seed = 1234
  beam_width = 5
  length_penalty = 0.6
  hidden_dim = 512
  
  emb = {
      'name': 'lookup_table',
      'dim': hidden_dim,
      'initializer': {
          'type': 'random_normal_initializer',
          'kwargs': {
              'mean': 0.0,
              'stddev': hidden_dim**-0.5,
          },
      }
  }
  
  position_embedder_hparams = {
      'dim': hidden_dim
  }
  
  encoder = {
      'dim': hidden_dim,
      'num_blocks': 6,
      'multihead_attention': {
          'num_heads': 8,
          'output_dim': hidden_dim
          # See documentation for more optional hyperparameters
      },
      'initializer': {
          'type': 'variance_scaling_initializer',
          'kwargs': {
              'scale': 1.0,
              'mode': 'fan_avg',
              'distribution': 'uniform',
          },
      },
      'poswise_feedforward': tx.modules.default_transformer_poswise_net_hparams(
          output_dim=hidden_dim)
  }
  
  decoder = copy.deepcopy(encoder)
  
  loss_label_confidence = 0.9
  
  opt = {
      'optimizer': {
          'type': 'AdamOptimizer',
          'kwargs': {
              'beta1': 0.9,
              'beta2': 0.997,
              'epsilon': 1e-9
          }
      }
  }
  
  lr = {
      'learning_rate_schedule': 'constant.linear_warmup.rsqrt_decay.rsqrt_depth',
      'lr_constant': 2 * (hidden_dim ** -0.5),
      'static_lr': 1e-3,
      'warmup_steps': 16000,
  }
  