class_docstr: Record GPU information in each worker and respond to resource allocation of GPUs.
  
  Attributes:
      worker_vacant_gpus (dict): Record how many vacant gpus does each
                                 worker has.
      worker_used_gpus (dict): Record how many used gpus does each
                                 worker has.
class_docstr_tok: ["Record","GPU","information","in","each","worker","and","respond","to","resource","allocation","of","GPUs",".","Attributes",":","worker_vacant_gpus","(","dict",")",":","Record","how","many","vacant","gpus","does","each","worker","has",".","worker_used_gpus","(","dict",")",":","Record","how","many","used","gpus","does","each","worker","has","."]
docstr: recycle n_gpu GPUs to the worker
          
docstr_tok: ["recycle","n_gpu","GPUs","to","the","worker"]
code: def recycle(self, worker_address, allocated_gpu):
      """recycle n_gpu GPUs to the worker"""
      for gpu_id in allocated_gpu.gpu.split(","):
          self.worker_vacant_gpus[worker_address].append(gpu_id)
          if gpu_id in self.worker_used_gpus[worker_address]:
              self.worker_used_gpus[worker_address].remove(gpu_id)
  
file_path: PARL/parl/remote/gpu_resource.py
imports: from collections import defaultdict
  from parl.remote.message import AllocatedGpu
label_desc: Use image node_id 6 for calling the GpuResource obj's underlying member method code with example usage: obj.recycle(worker_address, allocated_gpu) without return types