code: def prime_post_process(loader, merge_max_size):
      from heapq import heapify, heappop, heappush
  
      h = [
          (
              len(p),
              p,
          )
          for p in loader
      ]
      heapify(h)
  
      while len(h) > 1:
          len1, p1 = heappop(h)
          len2, p2 = heappop(h)
          if len1 + len2 <= merge_max_size:
              heappush(h, (len1 + len2, p1 + p2))
          else:
              heappush(
                  h,
                  (
                      len1,
                      p1,
                  ),
              )
              heappush(
                  h,
                  (
                      len2,
                      p2,
                  ),
              )
              break
  
      new_batch = []
  
      while len(h):
          _, p = heappop(h)
          new_batch.append(p)
  
      return new_batch
  
imports: import logging
  import math
  from typing import Callable, Iterator, List, NamedTuple, Optional, Tuple, Union
  import numpy
  import scipy.sparse
  import torch
  from torch import Tensor
  from tqdm import tqdm
  from torch_geometric.data import Data
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import get_ppr, is_undirected, subgraph
label_desc: Use image node_id 10 for calling a global function with example usage: prime_post_process(loader, merge_max_size) and returns: new_batch