code: def revnet_base():
      """Default hparams for Revnet."""
      hparams = common_hparams.basic_params1()
      hparams.add_hparam("num_channels", [64, 128, 256, 416])
      hparams.add_hparam("num_layers_per_block", [1, 1, 10, 1])
      hparams.add_hparam("bottleneck", True)
      hparams.add_hparam("first_batch_norm", [False, True, True, True])
      hparams.add_hparam("init_stride", 2)
      hparams.add_hparam("init_kernel_size", 7)
      hparams.add_hparam("init_maxpool", True)
      hparams.add_hparam("strides", [1, 2, 2, 2])
      hparams.add_hparam("num_channels_init_block", 64)
      hparams.add_hparam("dim", "2d")
  
      # 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
  
      # Can run with a batch size of 128 with Problem ImageImagenet224
      hparams.batch_size = 128
      return hparams
  
imports: import functools
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.utils import contrib
  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: revnet_base() and returns: hparams