code: def mtf_image_transformer_base():
      """Set of hyperparameters."""
      hparams = common_hparams.basic_params1()
      hparams.no_data_parallelism = True
      hparams.use_fixed_batch_size = True
      hparams.batch_size = 1
      hparams.max_length = 3072
      hparams.hidden_size = 256
      hparams.label_smoothing = 0.0
      # 8-way model-parallelism
      hparams.add_hparam("mesh_shape", "batch:8")
      hparams.add_hparam("layout", "batch:batch")
      hparams.add_hparam("mtf_mode", True)
      hparams.add_hparam("num_heads", 8)
      hparams.add_hparam("filter_size", 1024)
      hparams.add_hparam("num_encoder_layers", 0)
      hparams.add_hparam("num_decoder_layers", 6)
      hparams.add_hparam("attention_key_size", 256)
      hparams.add_hparam("attention_value_size", 256)
      # Share weights between input and target embeddings
      hparams.shared_embedding = True
  
      # mixture of experts hparams
      hparams.add_hparam("ffn_layer", "dense_relu_dense")
      hparams.add_hparam("moe_overhead_train", 1.0)
      hparams.add_hparam("moe_overhead_eval", 2.0)
      hparams.moe_num_experts = 16
      hparams.moe_loss_coef = 1e-3
  
      hparams.shared_embedding_and_softmax_weights = True
      hparams.optimizer = "Adafactor"
      hparams.learning_rate_schedule = "rsqrt_decay"
      hparams.learning_rate_warmup_steps = 10000
      hparams.add_hparam("d_kv", 64)
      hparams.add_hparam("d_ff", 2048)
  
      # Image related hparams
      hparams.add_hparam("img_len", 32)
      hparams.add_hparam("num_channels", 3)
      hparams.add_hparam("unconditional", True)
  
      # Local Attention related params
      hparams.add_hparam("block_length", 128)
      hparams.add_hparam("block_height", 16)
      hparams.add_hparam("block_width", 16)
      hparams.add_hparam("attention_type", "local1d")
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import mesh_tensorflow
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import mtf_model
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 5 for calling a global function with example usage: mtf_image_transformer_base() and returns: hparams