code: def sparse_assign_to_dense_blocks(
      xq,
      xb,
      xq_norms=None,
      xb_norms=None,
      qbs=16384,
      bbs=16384,
      nt=None,
  ):
      """
      decomposes the sparse_assign_to_dense function into blocks to avoid a
      possible memory blow up. Can be run in multithreaded mode, because scipy's
      sparse-dense matrix multiplication is single-threaded.
      """
      nq = xq.shape[0]
      nb = xb.shape[0]
      D = np.empty(nq, dtype="float32")
      D.fill(np.inf)
      I = -np.ones(nq, dtype=int)
  
      if xb_norms is None:
          xb_norms = (xb**2).sum(1)
  
      def handle_query_block(i):
          xq_block = xq[i : i + qbs]
          Iblock = I[i : i + qbs]
          Dblock = D[i : i + qbs]
          if xq_norms is None:
              xq_norms_block = np.array(xq_block.power(2).sum(1))
          else:
              xq_norms_block = xq_norms[i : i + qbs]
          for j in range(0, nb, bbs):
              Di, Ii = sparse_assign_to_dense(
                  xq_block,
                  xb[j : j + bbs],
                  xq_norms=xq_norms_block,
                  xb_norms=xb_norms[j : j + bbs],
              )
              if j == 0:
                  Iblock[:] = Ii
                  Dblock[:] = Di
              else:
                  mask = Di < Dblock
                  Iblock[mask] = Ii[mask] + j
                  Dblock[mask] = Di[mask]
  
      if nt == 0 or nt == 1 or nq <= qbs:
          list(map(handle_query_block, range(0, nq, qbs)))
      else:
          pool = ThreadPool(nt)
          pool.map(handle_query_block, range(0, nq, qbs))
  
      return D, I
  
imports: import numpy
  import faiss
  import time
  from multiprocessing.pool import ThreadPool
label_desc: Use image node_id 5 for calling a global function with example usage: sparse_assign_to_dense_blocks(xq, xb, xq_norms, xb_norms, qbs, bbs, nt) and returns: D,  I