code: def zip_batch_any_list_list(
      zip_batch_list: List[int],
      result_list_1: List[List[Any]],
      result_list_2: List[List[Any]],
  ) -> List[List[Any]]:
      res_list: List[List[Any]] = torch.jit.annotate(
          List[List[Any]], []
      )
  
      elem1 = 0
      elem2 = 0
  
      for zipper in zip_batch_list:
          if zipper > 0:
              res_list.append(result_list_1[elem1])
              elem1 = elem1 + 1
          else:
              res_list.append(result_list_1[elem2])
              elem2 = elem2 + 1
  
      return res_list
  
imports: from typing import Any, Dict, List, Optional, Tuple
  import torch
  from pytext.torchscript.tensorizer.tensorizer import ScriptTensorizer
label_desc: Use image node_id 18 for calling a global function with example usage: zip_batch_any_list_list(zip_batch_list, result_list_1, result_list_2) and returns: res_list