code: def transformer_moe_2k():
      """Base transformers model with moe.
  
      Will have the following architecture:
      * No encoder.
        * Layer 0: a - sep  (self-attention - unmasked separable convolutions)
        * Layer 1: a - sep
        * Layer 2: a - sep
        * Layer 3: a - sep
        * Layer 4: a - sep
      * Decoder architecture:
        * Layer 0: a - a - sepm  (self-attention - enco/deco-attention - masked sep)
        * Layer 1: a - a - sepm
        * Layer 2: a - a - moe  (mixture of expert layers in the middle)
        * Layer 3: a - a - sepm
        * Layer 4: a - a - sepm
  
      Returns:
        hparams
      """
      hparams = transformer_moe_8k()
      hparams.batch_size = 2048
  
      hparams.default_ff = "sep"
  
      # hparams.layer_types contains the network architecture:
      encoder_archi = "a/a/a/a/a"
      decoder_archi = "a-sepm/a-sepm/a-moe/a-sepm/a-sepm"
      hparams.layer_types = "{}#{}".format(encoder_archi, decoder_archi)
  
      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 4 for calling a global function with example usage: transformer_moe_2k() and returns: hparams