class_docstr: Manages distributed hyperparameter optimisation across many gpus.
class_docstr_tok: ["Manages","distributed","hyperparameter","optimisation","across","many","gpus","."]
docstr: Load results from file and queue parameter combinations to try.
  
  Returns:
    Boolean indicating if results were successfully loaded.
docstr_tok: ["Load","results","from","file","and","queue","parameter","combinations","to","try",".","Returns",":","Boolean","indicating","if","results","were","successfully","loaded","."]
code: def load_results(self):
      """Load results from file and queue parameter combinations to try.
  
      Returns:
        Boolean indicating if results were successfully loaded.
      """
      success = super().load_results()
  
      if success:
          self.worker_search_queue = self._get_worker_search_queue()
  
      return success
  
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 8 for calling the DistributedHyperparamOptManager obj's underlying member method code with example usage: obj.load_results() and returns: success