class_docstr: Manages distributed hyperparameter optimisation across many gpus.
class_docstr_tok: ["Manages","distributed","hyperparameter","optimisation","across","many","gpus","."]
docstr: Generates the queue of param combinations for current worker.
  
  Returns:
    Queue of hyperparameter combinations outstanding.
docstr_tok: ["Generates","the","queue","of","param","combinations","for","current","worker",".","Returns",":","Queue","of","hyperparameter","combinations","outstanding","."]
code: def _get_worker_search_queue(self):
      """Generates the queue of param combinations for current worker.
  
      Returns:
        Queue of hyperparameter combinations outstanding.
      """
      global_df = self.assign_worker_numbers(self.global_hyperparam_df)
      worker_df = global_df[global_df["worker"] == self.worker_num]
  
      left_overs = [
          s for s in worker_df.index if s not in self.results.columns
      ]
  
      return Deque(left_overs)
  
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 9 for calling the DistributedHyperparamOptManager obj's underlying member method code with example usage: obj._get_worker_search_queue() and returns: Deque