code: def attention_lm_moe_translation():
      """Version to use for seq2seq."""
      hparams = attention_lm_moe_base()
      hparams.layer_preprocess_sequence = "n"
      hparams.layer_postprocess_sequence = "da"
      hparams.learning_rate = 0.4
      hparams.prepend_mode = "prepend_inputs_masked_attention"
      hparams.max_length = 512
      hparams.label_smoothing = 0.1
      hparams.layer_prepostprocess_dropout = 0.2
      hparams.num_hidden_layers = 6
      hparams.moe_layers = "0,1,2,3,4,5"
      hparams.shared_embedding_and_softmax_weights = True
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  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 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 26 for calling a global function with example usage: attention_lm_moe_translation() and returns: hparams