code: def basic_range1(ranged_hparams):
      """A basic range of hyperparameters."""
      rhp = ranged_hparams
      rhp.set_discrete("batch_size", [1024, 2048, 4096])
      rhp.set_discrete("num_hidden_layers", [1, 2, 3, 4, 5, 6])
      rhp.set_discrete(
          "hidden_size", [32, 64, 128, 256, 512], scale=rhp.LOG_SCALE
      )
      rhp.set_discrete("kernel_height", [1, 3, 5, 7])
      rhp.set_discrete("kernel_width", [1, 3, 5, 7])
      rhp.set_discrete("compress_steps", [0, 1, 2])
      rhp.set_float("dropout", 0.0, 0.5)
      rhp.set_float("weight_decay", 1e-4, 10.0, scale=rhp.LOG_SCALE)
      rhp.set_float("label_smoothing", 0.0, 0.2)
      rhp.set_float("clip_grad_norm", 0.01, 50.0, scale=rhp.LOG_SCALE)
      rhp.set_float("learning_rate", 0.005, 2.0, scale=rhp.LOG_SCALE)
      rhp.set_categorical(
          "initializer",
          ["uniform", "orthogonal", "uniform_unit_scaling"],
      )
      rhp.set_float("initializer_gain", 0.5, 3.5)
      rhp.set_categorical(
          "learning_rate_decay_scheme", ["none", "sqrt", "noam", "exp"]
      )
      rhp.set_float(
          "optimizer_adam_epsilon", 1e-7, 1e-2, scale=rhp.LOG_SCALE
      )
      rhp.set_float("optimizer_adam_beta1", 0.8, 0.9)
      rhp.set_float("optimizer_adam_beta2", 0.995, 0.999)
      rhp.set_categorical(
          "optimizer",
          [
              "adam",
              "adagrad",
              "momentum",
              "rms_prop",
              "sgd",
              "yellow_fin",
          ],
      )
  
file_path: tensor2tensor/tensor2tensor/layers/common_hparams.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from six.moves import zip
  from tensor2tensor.utils import hparam
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling a global function with example usage: basic_range1(ranged_hparams) without return types