class_docstr: Defines parameter ranges for tuning.
class_docstr_tok: ["Defines","parameter","ranges","for","tuning","."]
docstr: To list of dicts suitable for Cloud ML Engine hyperparameter tuning.
docstr_tok: ["To","list","of","dicts","suitable","for","Cloud","ML","Engine","hyperparameter","tuning","."]
code: def to_parameter_specs(self, name_prefix=""):
      """To list of dicts suitable for Cloud ML Engine hyperparameter tuning."""
      specs = []
      for name, categories, _ in self._categorical_params.values():
          spec = {
              "parameterName": name_prefix + name,
              "type": "CATEGORICAL",
              "categoricalValues": categories,
          }
          specs.append(spec)
  
      for (
          name,
          feasible_points,
          scale,
          _,
      ) in self._discrete_params.values():
          spec = {
              "parameterName": name_prefix + name,
              "type": "DISCRETE",
              "discreteValues": feasible_points,
          }
          if scale:
              spec["scaleType"] = self.SCALES_STR[scale]
          specs.append(spec)
  
      for (
          name,
          min_val,
          max_val,
          scale,
          _,
      ) in self._float_params.values():
          spec = {
              "parameterName": name_prefix + name,
              "type": "DOUBLE",
              "minValue": min_val,
              "maxValue": max_val,
          }
          if scale:
              spec["scaleType"] = self.SCALES_STR[scale]
          specs.append(spec)
  
      for name, min_val, max_val, scale, _ in self._int_params.values():
          spec = {
              "parameterName": name_prefix + name,
              "type": "INTEGER",
              "minValue": min_val,
              "maxValue": max_val,
          }
          if scale:
              spec["scaleType"] = self.SCALES_STR[scale]
          specs.append(spec)
  
      return specs
  
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 8 for calling the RangedHParams obj's underlying member method code with example usage: obj.to_parameter_specs(name_prefix) and returns: specs