class_docstr: Manages distributed hyperparameter optimisation across many gpus.
class_docstr_tok: ["Manages","distributed","hyperparameter","optimisation","across","many","gpus","."]
docstr: Generates actual hyperparameter combinations.
  
  Returns:
    DataFrame containing hyperparameter combinations.
docstr_tok: ["Generates","actual","hyperparameter","combinations",".","Returns",":","DataFrame","containing","hyperparameter","combinations","."]
code: def _generate_full_hyperparam_df(self):
      """Generates actual hyperparameter combinations.
  
      Returns:
        DataFrame containing hyperparameter combinations.
      """
  
      np.random.seed(131)  # for reproducibility of hyperparam list
  
      name_list = []
      param_list = []
      for _ in range(self.total_search_iterations):
          params = super().get_next_parameters(name_list)
  
          name = self._get_name(params)
  
          name_list.append(name)
          param_list.append(params)
  
      full_search_df = pd.DataFrame(param_list, index=name_list)
  
      return full_search_df
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import os
  import shutil
  import libs.utils
  import numpy
  import pandas
label_desc: Use image node_id 6 for calling the DistributedHyperparamOptManager obj's underlying member method code with example usage: obj._generate_full_hyperparam_df() and returns: full_search_df