code: def transformer_moe_8k_lm():
      """Language modeling params.
  
      Will have the following architecture by default:
      * No encoder.
      * Decoder architecture:
        * Layer 0: a - sepm  (masked self-attention/masked separable convolutions)
        * Layer 1: a - sepm
        * Layer 2: a - moe  (mixture of expert layers in the middle)
        * Layer 3: a - sepm
        * Layer 4: a - sepm
  
      Returns:
        hparams
      """
      hparams = transformer_moe_8k()
  
      # Use masked versions of local attention and separable convolution
      hparams.default_ff = "sepm"
  
      # hparams.layer_types contains the network architecture:
      # Start with '#' for decoder only architecture
      hparams.layer_types = (
          "#a/a/a-moe/a/a"  # 5 full attention layers with 1 moe
      )
      # For long sequences, if running out of memory, it's possible to use the
      # one of those two optimized versions instead:
      #  * Memory efficient multihead attention (slow):
      # hparams.layer_types = "#mem/mem/mem-moe/mem/mem"
      #  * Alternate between local/compressed attention layers (faster):
      # hparams.layer_types = "#locm/redm/locm-moe/redm/locm"
  
      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.models import transformer
  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 3 for calling a global function with example usage: transformer_moe_8k_lm() and returns: hparams