code: def mtf_resnet_base():
      """Set of hyperparameters."""
      hparams = common_hparams.basic_params1()
      hparams.no_data_parallelism = True
      hparams.use_fixed_batch_size = True
      hparams.batch_size = 32
      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("filter_size", 1024)
  
      hparams.add_hparam("num_layers", 6)
      # Share weights between input and target embeddings
      hparams.shared_embedding = True
  
      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", 32)
  
      # Image related hparams
      hparams.add_hparam("img_len", 32)
      hparams.add_hparam("num_channels", 3)
      hparams.add_hparam("row_blocks", 1)
      hparams.add_hparam("col_blocks", 1)
      hparams.add_hparam("rows_size", 32)
      hparams.add_hparam("cols_size", 32)
  
      # Model-specific parameters
      hparams.add_hparam("layer_sizes", [3, 4, 6, 3])
      hparams.add_hparam("filter_sizes", [64, 64, 128, 256, 512])
      hparams.add_hparam("is_cifar", False)
  
      # Variable init
      hparams.initializer = "normal_unit_scaling"
      hparams.initializer_gain = 2.0
  
      # TODO(nikip): Change optimization scheme?
      hparams.learning_rate = 0.1
      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.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 4 for calling a global function with example usage: mtf_resnet_base() and returns: hparams