code: def xmoe_tr_1d():
      """Mixture of experts (16 experts).
  
  
      623M Params, einsum=1.09e13
  
      Returns:
        a hparams
      """
      hparams = xmoe_tr_dense_2k()
      hparams.encoder_layers = ["self_att", "moe_1d"] * 4
      hparams.decoder_layers = ["self_att", "enc_att", "moe_1d"] * 4
      hparams.layout = "batch:batch;experts:batch"
      hparams.moe_hidden_size = 2048
      hparams.moe_num_experts = 16
      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 3 for calling a global function with example usage: xmoe_tr_1d() and returns: hparams