code: def aligned_base():
      """Set of hyperparameters.
  
      languagemodel_wiki_scramble1k50, 1gpu, 7k steps (10min): log(ppl)_eval = 2.60
      12.0 steps/sec on P100
      8gpu (8x batch), 7k steps: log(ppl)_eval = 2.00
  
      Returns:
        a hparams object
      """
      hparams = common_hparams.basic_params1()
      hparams.force_full_predict = True
      hparams.hidden_size = 512
      hparams.batch_size = 5000
      hparams.max_length = 0
      hparams.min_length_bucket = 1024
      hparams.dropout = 0.0
      hparams.layer_prepostprocess_dropout = 0.0
      hparams.label_smoothing = 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.initializer = "uniform_unit_scaling"
      hparams.weight_decay = 0.0
      hparams.optimizer_adam_beta1 = 0.9
      hparams.optimizer_adam_beta2 = 0.98
      hparams.shared_embedding_and_softmax_weights = True
      hparams.add_hparam(
          "ffn_hidden_sizes", "2048"
      )  # Add new ones like this.
      hparams.moe_num_experts = 32
      hparams.layer_preprocess_sequence = "n"
      hparams.layer_postprocess_sequence = "da"
      hparams.add_hparam("layers", "timing," + "conv,att,ffn," * 2)
  
      # 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("pos", "timing")  # timing, none
      # moe params. local attention moe.
      hparams.add_hparam("attention_local", False)
      hparams.add_hparam("attention_moe_k", 2)
      hparams.add_hparam("attention_num_experts", 16)
      hparams.add_hparam("attention_split_batch", False)
      # Key, query and value dimensions for the attention
      hparams.add_hparam("attention_kq_size", 128)
      hparams.add_hparam("attention_v_size", 256)
      # Loss coef for load balancing
      hparams.add_hparam("attention_load_balance", 2e-2)
      hparams.add_hparam("diet_experts", False)
      hparams.add_hparam("memory_efficient_ffn", False)
      hparams.add_hparam("local_attention_window", 128)
      hparams.add_hparam("attention_num_groups", 8)
      hparams.add_hparam("memory_target_density", 2.0)
      hparams.add_hparam("multiplicative_overhead", 1.25)
      hparams.add_hparam("multiplicative_overhead_eval", 2.0)
      hparams.add_hparam("attention_image_summary", True)
      # LSH params
      hparams.add_hparam("lsh_truncated", True)
      # For testing right-masking.
      # This is not implemented in all layers.
      hparams.add_hparam("mask_right", False)
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import expert_utils
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 4 for calling a global function with example usage: aligned_base() and returns: hparams