code: def resnet_base():
      """Set of hyperparameters."""
      # For imagenet on TPU:
      # Set train_steps=120000
      # Set eval_steps=48
  
      # Base
      hparams = common_hparams.basic_params1()
  
      # Model-specific parameters
      hparams.add_hparam("layer_sizes", [3, 4, 6, 3])
      hparams.add_hparam("bottleneck_ratios", [4, 4, 4, 4])
      hparams.add_hparam("filter_sizes", [64, 64, 128, 256, 512])
      hparams.add_hparam("block_fn", "bottleneck")
      hparams.add_hparam("use_nchw", True)
      hparams.add_hparam("is_cifar", False)
  
      # Targeted dropout
      hparams.add_hparam("use_td", False)
      hparams.add_hparam("targeting_rate", None)
      hparams.add_hparam("keep_prob", None)
  
      # Variable init
      hparams.initializer = "normal_unit_scaling"
      hparams.initializer_gain = 2.0
  
      # Optimization
      hparams.optimizer = "Momentum"
      hparams.optimizer_momentum_momentum = 0.9
      hparams.optimizer_momentum_nesterov = True
      hparams.weight_decay = 1e-4
      hparams.clip_grad_norm = 0.0
      # (base_lr=0.1) * (batch_size=128*8 (on TPU, or 8 GPUs)=1024) / (256.)
      hparams.learning_rate = 0.4
      hparams.learning_rate_decay_scheme = "cosine"
      # For image_imagenet224, 120k training steps, which effectively makes this a
      # cosine decay (i.e. no cycles).
      hparams.learning_rate_cosine_cycle_steps = 120000
  
      hparams.batch_size = 128
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import hparam
  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 9 for calling a global function with example usage: resnet_base() and returns: hparams