code: def _sim_block(block, bsp, bep, rmh, min_sim, max_nbrs, nitems):
      "Compute a single block of the similarity matrix"
      # assert block.nrows == bep - bsp
  
      bitems = block.nrows
      if block.nnz == 0:
          return create_empty(bitems, nitems)
  
      # create a matrix handle for the subset matrix
      amh = csrk.to_handle(block)
  
      smh = csrk.mult_abt(rmh, amh)
  
      csrk.release_handle(amh)
  
      csrk.order_columns(smh)
      block_csr = csrk.from_handle(smh)
      # this is allowed now
      csrk.release_handle(smh)
  
      block_csr = _trim_sim_block(
          nitems, bsp, bitems, block_csr, min_sim, max_nbrs
      )
  
      return block_csr
  
imports: from sys import intern
  import logging
  import warnings
  import pandas
  import numpy
  import scipy.sparse
  import scipy.sparse.linalg
  from csr import CSR, create_from_sizes, create_empty
  import csr.kernel
  from numba import njit, prange
  from numba.typed import List
  from lenskit import util, DataWarning, ConfigWarning
  from lenskit.data import sparse_ratings
  from lenskit.sharing import in_share_context
  from lenskit.util.parallel import is_mp_worker
  from lenskit.util.accum import kvp_minheap_insert, kvp_minheap_sort
  from .None import Predictor
label_desc: Use image node_id 4 for calling a global function with example usage: _sim_block(block, bsp, bep, rmh, min_sim, max_nbrs, nitems) and returns: block_csr, create_empty