code: def precise_dist_share_mem(
      feat,
      nbrs,
      dist,
      num_process=16,
      sort=True,
      process_unit=4000,
      verbose=False,
  ):
      from torch import multiprocessing as mp
  
      num, _ = feat.shape
      num_per_proc = int(num / num_process) + 1
  
      for pi in range(num_process):
          sid = pi * num_per_proc
          eid = min(sid + num_per_proc, num)
  
          kwargs = {
              "feat": feat,
              "nbrs": nbrs,
              "dist": dist,
              "sid": sid,
              "eid": eid,
              "sort": sort,
              "process_unit": process_unit,
              "verbose": verbose,
          }
          bmm(**kwargs)
  
imports: import gc
  from tqdm import tqdm
  from .faiss_gpu import faiss_search_approx_knn
label_desc: Use image node_id 2 for calling a global function with example usage: precise_dist_share_mem(feat, nbrs, dist, num_process, sort, process_unit, verbose) without return types