class_docstr: Record CPU information in each worker and respond to resource allocation of CPUs.
  
  Attributes:
      worker_vacant_cpus (dict): Record how many vacant cpus does each
                                 worker has.
      worker_used_cpus (dict): Record how many used cpus does each
                                 worker has.
class_docstr_tok: ["Record","CPU","information","in","each","worker","and","respond","to","resource","allocation","of","CPUs",".","Attributes",":","worker_vacant_cpus","(","dict",")",":","Record","how","many","vacant","cpus","does","each","worker","has",".","worker_used_cpus","(","dict",")",":","Record","how","many","used","cpus","does","each","worker","has","."]
docstr: recycle n_cpu CPUs to the worker
          
docstr_tok: ["recycle","n_cpu","CPUs","to","the","worker"]
code: def recycle(self, worker_address, allocated_cpu):
      """recycle n_cpu CPUs to the worker"""
      self.worker_vacant_cpus[worker_address] += allocated_cpu.n_cpu
      self.worker_used_cpus[worker_address] -= allocated_cpu.n_cpu
      assert self.worker_used_cpus[worker_address] >= 0
  
file_path: PARL/parl/remote/cpu_resource.py
imports: from collections import defaultdict
  from parl.remote.message import AllocatedCpu
label_desc: Use image node_id 6 for calling the CpuResource obj's underlying member method code with example usage: obj.recycle(worker_address, allocated_cpu) without return types