code: def attention_lm_ae_extended():
      """Experiment with the exp_factor params."""
      hparams = attention_lm_moe_base_long_seq()
      hparams.attention_layers = "eeee"
      hparams.attention_local = True
      # hparams.factored_logits=1  # Necessary when the number of expert grow bigger
      hparams.attention_moe_k = 2
      hparams.attention_exp_factor = 4
      # hparams.attention_exp_inputdim = 128
  
      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 15 for calling a global function with example usage: attention_lm_ae_extended() and returns: hparams