code: def wiki_2x2_base():
      """Set of architectural experiments - language model on wikipedia on a 2x2.
  
      1 epoch = ~180k steps at batch size 32 - we may never finish an epoch!
  
      Returns:
        a hparams
      """
      hparams = mtf_transformer.mtf_transformer_base_lm()
      hparams.shared_embedding_and_softmax_weights = False
      # no dropout - dataset is big enough to avoid overfitting.
      hparams.attention_dropout = 0.0
      hparams.relu_dropout = 0.0
      hparams.layer_prepostprocess_dropout = 0.0
      hparams.max_length = 1024
      # 4 sequences per core
      hparams.batch_size = 32
      # We don't use linear decay in these experiments, since we don't want
      # a sharp jump in quality at the end of the training schedule.
      # You can insert this once you find the right architecture.
      hparams.learning_rate_schedule = "rsqrt_decay"
      hparams.mesh_shape = "all:8"
      hparams.layout = "batch:all;experts:all"
  
      # parameters for mixture-of-experts
      moe.set_default_moe_hparams(hparams)
      hparams.moe_num_experts = 16
      hparams.moe_hidden_size = 8192
  
      hparams.decoder_layers = ["att", "drd"] * 6
      hparams.d_model = 1024
      hparams.d_ff = 2048
      hparams.d_kv = 128
      hparams.num_heads = 4
  
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.models import mtf_transformer
  from tensor2tensor.models import mtf_transformer2
  from tensor2tensor.models.research import moe
  from tensor2tensor.utils import registry
label_desc: Use image node_id 27 for calling a global function with example usage: wiki_2x2_base() and returns: hparams