code: def calculate_gains_sieve(func, dtypes, parallel, fastmath, cache):
      @njit(dtypes, parallel=parallel, fastmath=fastmath, cache=cache)
      def calculate_gains_sieve_(
          X,
          k,
          current_values,
          selections,
          gains,
          total_gains,
          max_values,
          n_selected,
          idxs,
      ):
          n = X.shape[0]
          d = max_values.shape[0]
  
          for j in prange(d):
              for i in range(n):
                  if n_selected[j] == k:
                      break
  
                  idx = idxs[i]
                  threshold = (max_values[j] / 2.0 - total_gains[j]) / (
                      k - n_selected[j]
                  )
                  gain = (
                      func(current_values[j] + X[i]).sum()
                      - total_gains[j]
                  )
  
                  if gain > threshold:
                      current_values[j] += X[i]
                      total_gains[j] += gain
  
                      selections[j, n_selected[j]] = idx
                      gains[j, n_selected[j]] = gain
                      n_selected[j] += 1
  
      return calculate_gains_sieve_
  
imports: import math
  import numpy
  from .base import BaseSelection
  from ..optimizers import LazyGreedy
  from ..optimizers import ApproximateLazyGreedy
  from tqdm import tqdm
  from numba import njit
  from numba import prange
  import torch
label_desc: Use image node_id 4 for calling a global function with example usage: calculate_gains_sieve(func, dtypes, parallel, fastmath, cache) and returns: calculate_gains_sieve_