code: def xmoe2_v1():
      """Model incorporating mixture-of-experts and local-attention.
  
      ~6B parameters
  
      32 experts in 3 hierarchichal moe layers.
  
      Returns:
        a hparams
      """
      hparams = xmoe2_dense(0)
      moe.set_default_moe_hparams(hparams)
      hparams.decoder_layers = (
          [
              "local_att",
              "local_att",
              "drd",
              "att",
              "drd",
              "local_att",
              "local_att",
              "hmoe",
          ]
          * 4
      )[:-1]
      hparams.d_ff = 2048
      hparams.d_kv = 128
      hparams.moe_hidden_size = 32768
      hparams.mesh_shape = "b0:4;b1:8"
      hparams.layout = (
          "outer_batch:b0;inner_batch:b1,expert_x:b1,expert_y:b0"
      )
      hparams.outer_batch_size = 4
      hparams.moe_num_experts = [8, 4]
      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 19 for calling a global function with example usage: xmoe2_v1() and returns: hparams