code: def attention_lm_moe_base_ae():
      """Base model with attention expert."""
      hparams = attention_lm_moe_base_long_seq()
      hparams.attention_type = AttentionType.LOCAL_EXPERTS
  
      hparams.learning_rate = 0.05
      hparams.learning_rate_warmup_steps = 10000
      # According to noam, ("n", "da") seems better for harder-to-learn models
      # hparams.layer_preprocess_sequence = "n"
      # hparams.layer_postprocess_sequence = "da"
      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 8 for calling a global function with example usage: attention_lm_moe_base_ae() and returns: hparams