code: def xmoe_tr_2d():
      """Mixture of experts (16 experts).
  
      623M Params, einsum=1.09e13
  
      Returns:
        a hparams
      """
      hparams = xmoe_tr_dense_2k()
      hparams.mesh_shape = "b0:2;b1:4"
      hparams.outer_batch_size = 4
      hparams.layout = (
          "outer_batch:b0;inner_batch:b1,expert_x:b1,expert_y:b0"
      )
      hparams.encoder_layers = ["self_att", "moe_2d"] * 4
      hparams.decoder_layers = ["self_att", "enc_att", "moe_2d"] * 4
      hparams.moe_hidden_size = 2048
      hparams.moe_experts_x = 4
      hparams.moe_experts_y = 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 4 for calling a global function with example usage: xmoe_tr_2d() and returns: hparams