code: def init_pytorch_worker(
      rank, devices, manager_ip, manager_port
  ) -> None:
      import cupy
      import rmm
  
      device_id = devices[rank]
  
      rmm.reinitialize(
          devices=[device_id],
          pool_allocator=False,
      )
  
      # torch.cuda.change_current_allocator(rmm.rmm_torch_allocator)
      # cupy.cuda.set_allocator(rmm.rmm_cupy_allocator)
  
      cupy.cuda.Device(device_id).use()
      torch.cuda.set_device(device_id)
  
      # Pytorch training worker initialization
      dist_init_method = f"tcp://{manager_ip}:{manager_port}"
  
      torch.distributed.init_process_group(
          backend="nccl",
          init_method=dist_init_method,
          world_size=len(devices),
          rank=rank,
      )
  
imports: from ogb.nodeproppred import NodePropPredDataset
  import time
  import argparse
  import gc
  import torch
  import numpy
  from torch_geometric.nn import CuGraphSAGEConv
  import torch.nn
  import torch.nn.functional
  import torch.distributed
  import torch.multiprocessing
  from torch.nn.parallel import DistributedDataParallel
  from typing import List
label_desc: Use image node_id 2 for calling a global function with example usage: init_pytorch_worker(rank, devices, manager_ip, manager_port) without return types