code: def attention_lm_moe_base():
      """Set of hyperparameters.
  
      suitable for 1 gpu.
      on lm1b_32k:
         ~229M params
         0.9 steps/sec on  [GeForce GTX TITAN X]
  
      Returns:
        a hparams object
      """
      hparams = common_hparams.basic_params1()
      hparams.hidden_size = 1024
      hparams.batch_size = 8192
      hparams.max_length = 256
      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 = 4
      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 = False
      hparams.add_hparam("filter_size", 2048)  # Add new ones like this.
      hparams.moe_num_experts = 32
      # attention-related flags
      hparams.add_hparam("num_heads", 8)
      hparams.add_hparam("attention_key_channels", 0)
      hparams.add_hparam("attention_value_channels", 0)
      # All hyperparameters ending in "dropout" are automatically set to 0.0
      # when not in training mode.
      hparams.add_hparam("attention_dropout", 0.0)
      hparams.add_hparam("relu_dropout", 0.0)
      hparams.add_hparam("pos", "timing")  # timing, none
      hparams.add_hparam(
          "moe_layers", "2"
      )  # comma separated list of layer numbers
      # moe params. local attention moe.
      # If attention_layers is set, the num_hidden_layers parameter will be ignored
      # and each caracter of the string will correspond to one attention
      # layer type
      hparams.add_hparam("attention_layers", "")
      hparams.add_hparam("attention_type", AttentionType.MULTIHEAD)
      hparams.add_hparam("attention_local", False)
      hparams.add_hparam("attention_moe_k", 2)
      hparams.add_hparam("attention_num_head", 1)
      hparams.add_hparam("attention_num_experts", 16)
      hparams.add_hparam("attention_split_batch", False)
      hparams.add_hparam("attention_red_factor", 3)
      hparams.add_hparam("attention_block_length", 128)
      hparams.add_hparam("attention_reduction_type", "conv")
      # Non linearity for the attention reduction. Either "none", or "silu" (
      # Sigmoid Linear-Unit described in https://arxiv.org/abs/1710.05941)
      hparams.add_hparam("attention_nonlinearity", "none")
      # If attention_exp_factor is set, each input to local_expert_attention (of
      # dimensionality hidden size) is projected into attention_exp_factor smaller
      # inputs, each of dimensionality attention_exp_inputdim. (otherwise
      # attention_exp_inputdim is ignored)
      hparams.add_hparam("attention_exp_factor", 0)
      hparams.add_hparam("attention_exp_inputdim", 128)
      # Key, query and value dimensions for the attention
      hparams.add_hparam("attention_kq_size", 128)
      hparams.add_hparam("attention_v_size", 256)
      # Loss coef for load balancing
      hparams.add_hparam("attention_load_balance", 2e-2)
      # Locality-sensitive hashing params
      hparams.add_hparam("lsh_num_hyperplanes", 4)
      hparams.add_hparam("lsh_use_map_fn", False)
  
      hparams.add_hparam("use_sepconv", False)
      hparams.add_hparam("diet_experts", False)
      hparams.add_hparam("memory_efficient_ffn", False)
      # if True, we learn a non-autoregressive model from "inputs" to "targets".
      # if False, we learn an autoregressive model to generate "targets"
      hparams.add_hparam("use_inputs", False)
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  from six.moves import range
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  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: attention_lm_moe_base() and returns: hparams