code: def vqa_attention_base_range(rhp):
      """Small range of hyperparameters."""
      # After starting from base, set intervals for some parameters.
      rhp.set_float("learning_rate", 0.1, 1.0, scale=rhp.LOG_SCALE)
      rhp.set_float("clip_grad_norm", 0.1, 10, scale=rhp.LOG_SCALE)
      rhp.set_discrete("batch_size", [128, 256, 512, 1024])
      rhp.set_float("weight_decay", 0.0, 1e-4)
      rhp.set_categorical("rnn_type", ["lstm", "lstm_layernorm"])
  
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_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import vqa_layers
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  from tensorflow.contrib import rnn
  from tensorflow.contrib.layers.python.layers import utils
  from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_152
  from tensorflow.contrib.slim.python.slim.nets.resnet_v2 import resnet_v2_152
label_desc: Use image node_id 31 for calling a global function with example usage: vqa_attention_base_range(rhp) without return types