class_docstr: This class is initialized with the name of a json configuration file.
  The config contains information about a classification method and the
  hyperparameter arguments it needs to run. Its main purpose is to generate
  hyperpartitions (possible combinations of categorical hyperparameters).
docstr: Sort a list of HyperParameter objects into lists of constants,
  categoricals, and tunables.
docstr_tok: ["Sort","a","list","of","HyperParameter","objects","into","lists","of","constants",",","categoricals",",","and","tunables","."]
code: def _sort_parameters(self, params):
      """
      Sort a list of HyperParameter objects into lists of constants,
      categoricals, and tunables.
      """
      constants = []
      categoricals = []
      tunables = []
      for p in params:
          param = self.parameters[p]
          if param.is_constant:
              if param.is_categorical:
                  constants.append((p, param.values[0]))
              else:
                  constants.append((p, param.range[0]))
          elif param.is_categorical:
              categoricals.append(p)
          else:
              tunables.append((p, param.as_tunable()))
  
      return constants, categoricals, tunables
  
imports: from __future__ import absolute_import, unicode_literals
  import json
  import os
  from builtins import object, range
  from builtins import str
  import btb
  from atm.constants import METHODS
label_desc: Use image node_id 2 for calling the Method obj's underlying member method code with example usage: obj._sort_parameters(params) and returns: constants,  categoricals,  tunables