code: def transformer_moe_prepend_8k():
      """Model which formulate a seq2seq problem as language modeling."""
      hparams = transformer_moe_8k()
      hparams.prepend_mode = "prepend_inputs_masked_attention"
      hparams.eval_drop_long_sequences = False
      hparams.max_input_seq_length = 7500
      hparams.default_ff = "sepm"
      hparams.layer_types = "locm/redm/locm-moe/redm/locm"
      hparams.moe_num_experts = 256
      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 6 for calling a global function with example usage: transformer_moe_prepend_8k() and returns: hparams