class_docstr: support sync of multiple gpus/nodes.
class_docstr_tok: ["support","sync","of","multiple","gpus\/nodes","."]
code: def build_retriver(
      self,
      retriever_cls=None,
      hidden_size=None,
      centroids=4096,
      db_type="flatl2",
      examples_per_cent_to_train=48,
  ):
      if retriever_cls is None:
          retriever_cls = self.retriever_cls
      if hidden_size is None:
          hidden_size = self.hidden_size
      """merge results from multiple gpus and return a retriver.."""
      if torch.distributed.is_initialized():
          self.save()
          # sync saving.
          torch.distributed.barrier()
          world_size = torch.distributed.get_world_size()
      else:
          world_size = 1
      self.retriver = retriever_cls(
          hidden_size, centroids, db_type, examples_per_cent_to_train
      )
      # each gpu process has its own retriever.
      for local_rank in range(world_size):
          if get_local_rank() == 0:
              print("load local_rank", local_rank)
          hidden_states, video_ids = self.load(local_rank)
          hidden_states = hidden_states.astype("float32")
          self.retriver.add(hidden_states, video_ids)
      return self.retriver
  
imports: import torch
  import os
  import numpy
  import pickle
  from .None import retri
  from ..utils import get_local_rank
label_desc: Use image node_id 2 for calling the DistributedVectorPool obj's underlying member method code with example usage: obj.build_retriver(retriever_cls, hidden_size, centroids, db_type, examples_per_cent_to_train) and returns: self