code: def transformer_moe_base():
      """Set of hyperparameters."""
      hparams = common_hparams.basic_params1()
      hparams.norm_type = "layer"
      hparams.hidden_size = 512
      hparams.batch_size = 4096
      hparams.max_length = 2001
      hparams.max_input_seq_length = 2000
      hparams.max_target_seq_length = 2000
      hparams.dropout = 0.0
      hparams.clip_grad_norm = 0.0  # i.e. no gradient clipping
      hparams.optimizer_adam_epsilon = 1e-9
      hparams.learning_rate_decay_scheme = "noam"
      hparams.learning_rate = 0.1
      hparams.learning_rate_warmup_steps = 2000
      hparams.initializer_gain = 1.0
      hparams.num_hidden_layers = 5
      hparams.initializer = "uniform_unit_scaling"
      hparams.weight_decay = 0.0
      hparams.optimizer_adam_beta1 = 0.9
      hparams.optimizer_adam_beta2 = 0.98
      hparams.num_sampled_classes = 0
      hparams.label_smoothing = 0.0
      hparams.shared_embedding_and_softmax_weights = True
      # According to noam, ("n", "da") seems better for harder-to-learn models
      hparams.layer_preprocess_sequence = "n"
      hparams.layer_postprocess_sequence = "da"
  
      # Hparams used by transformer_prepare_decoder() function
      hparams.add_hparam("pos", "timing")  # timing, none
      hparams.add_hparam("proximity_bias", False)
      hparams.add_hparam("causal_decoder_self_attention", True)
  
      hparams = common_attention.add_standard_attention_hparams(hparams)
  
      # Decoder layers type. If set, num_decoder_layers parameter will be ignored
      # and the number of decoder layer will be deduced from the string
      # See top file comment for example of usage
      hparams.add_hparam("layer_types", "")
      # Default attention type (ex: a, loc, red,...) and feed-forward type (ex: fc,
      # sep, moe,...)
      hparams.add_hparam("default_att", "a")
      hparams.add_hparam("default_ff", "fc")
  
      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 1 for calling a global function with example usage: transformer_moe_base() and returns: hparams