code: def adaptive_universal_transformer_base_range(rhp):
      """Range of hyperparameters."""
      # After starting from base, set intervals for some parameters.
      rhp.set_discrete("act_max_steps", [8, 16, 32])
      rhp.set_float("act_loss_weight", 0.0, 0.5)
      rhp.set_discrete("hidden_size", [1024, 2048, 4096])
      rhp.set_discrete("filter_size", [2048, 4096, 8192])
      rhp.set_discrete("num_heads", [8, 16, 32])
      rhp.set_categorical("transformer_ffn_type", ["sepconv", "fc"])
      rhp.set_float("learning_rate", 0.3, 3.0, scale=rhp.LOG_SCALE)
      rhp.set_float("weight_decay", 0.0, 2.0)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.models import transformer
  from tensor2tensor.models.research import universal_transformer_util
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 46 for calling a global function with example usage: adaptive_universal_transformer_base_range(rhp) without return types