class_docstr: The worker is responsible for evaluating a single configuration on a single budget at a time.
  Communication to the individual workers goes via the nameserver, management of the worker-pool and job
  scheduling is done by the Dispatcher and jobs are determined by the Master. In distributed systems, each
  cluster-node runs a Worker-instance. To implement your own worker, overwrite the `__init__`- and the `compute`-method.
  The first allows to perform inital computations, e.g. loading the dataset, when the worker is started, while the
  latter is repeatedly called during the optimization and evaluates a given configuration yielding the associated loss.
class_docstr_tok: ["The","worker","is","responsible","for","evaluating","a","single","configuration","on","a","single","budget","at","a","time",".","Communication","to","the","individual","workers","goes","via","the","nameserver",",","management","of","the","worker-pool","and","job","scheduling","is","done","by","the","Dispatcher","and","jobs","are","determined","by","the","Master",".","In","distributed","systems",",","each","cluster-node","runs","a","Worker-instance",".","To","implement","your","own","worker",",","overwrite","the","`","__init__","`","-","and","the","`","compute","`","-method",".","The","first","allows","to","perform","inital","computations",",","e.g",".","loading","the","dataset",",","when","the","worker","is","started",",","while","the","latter","is","repeatedly","called","during","the","optimization","and","evaluates","a","given","configuration","yielding","the","associated","loss","."]
code: def is_busy(self):
      return self.busy
  
file_path: HpBandSter/hpbandster/core/worker.py
imports: import time
  import logging
  import pickle
  import os, socket
  import traceback
  import threading
  import Pyro4
label_desc: Use image node_id 7 for calling the Worker obj's underlying member method code with example usage: obj.is_busy() and returns: self