class_docstr: A wrapper for RPC callee that will perform RPC sampling from remote
  processes.
class_docstr_tok: ["A","wrapper","for","RPC","callee","that","will","perform","RPC","sampling","from","remote","processes","."]
code: def __init__(self, sampler: NeighborSampler):
      super().__init__()
      self.sampler = sampler
  
imports: import itertools
  import logging
  import math
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.multiprocessing
  from torch import Tensor
  from torch_geometric.distributed import LocalFeatureStore, LocalGraphStore
  from torch_geometric.distributed.dist_context import DistContext
  from torch_geometric.distributed.event_loop import ConcurrentEventLoop, to_asyncio_future
  from torch_geometric.distributed.rpc import RPCCallBase, RPCRouter, rpc_async, rpc_partition_to_workers, rpc_register
  from torch_geometric.distributed.utils import BatchDict, NodeDict, remove_duplicates
  from torch_geometric.sampler import EdgeSamplerInput, HeteroSamplerOutput, NegativeSampling, NeighborSampler, NodeSamplerInput, SamplerOutput
  from torch_geometric.sampler.base import NumNeighbors, SubgraphType
  from torch_geometric.sampler.neighbor_sampler import neg_sample
  from torch_geometric.sampler.utils import remap_keys
  from torch_geometric.typing import EdgeType, NodeType
label_desc: Use image node_id 1 to create a new RPCSamplingCallee object from inherited base classes: RPCCallBase with example: obj = RPCSamplingCallee(sampler)