code: def _merge_graphs(
      supergraph,
      nodes,
      subgraph,
      nodeid,
      iterables,
      prefix,
      synchronize=False,
  ):
      """Merges two graphs that share a subset of nodes.
  
      If the subgraph needs to be replicated for multiple iterables, the
      merge happens with every copy of the subgraph. Assumes that edges
      between nodes of supergraph and subgraph contain data.
  
      Parameters
      ----------
      supergraph : networkx graph
      Parent graph from which subgraph was selected
      nodes : networkx nodes
      Nodes of the parent graph from which the subgraph was initially
      constructed.
      subgraph : networkx graph
      A subgraph that contains as a subset nodes from the supergraph.
      These nodes connect the subgraph to the supergraph
      nodeid : string
      Identifier of a node for which parameterization has been sought
      iterables : dict of functions
      see `pipeline.NodeWrapper` for iterable requirements
  
      Returns
      -------
      Returns a merged graph containing copies of the subgraph with
      appropriate edge connections to the supergraph.
  
      """
      # Retrieve edge information connecting nodes of the subgraph to other
      # nodes of the supergraph.
      supernodes = supergraph.nodes()
      ids = [n._hierarchy + n._id for n in supernodes]
      if len(set(ids)) != len(ids):
          # This should trap the problem of miswiring when multiple iterables are
          # used at the same level. The use of the template below for naming
          # updates to nodes is the general solution.
          raise Exception(
              "Execution graph does not have a unique set of node "
              "names. Please rerun the workflow"
          )
      edgeinfo = {}
      for n in list(subgraph.nodes()):
          nidx = ids.index(n._hierarchy + n._id)
          for edge in supergraph.in_edges(list(supernodes)[nidx]):
              # make sure edge is not part of subgraph
              if edge[0] not in subgraph.nodes():
                  if n._hierarchy + n._id not in list(edgeinfo.keys()):
                      edgeinfo[n._hierarchy + n._id] = []
                  edgeinfo[n._hierarchy + n._id].append(
                      (edge[0], supergraph.get_edge_data(*edge))
                  )
      supergraph.remove_nodes_from(nodes)
      # Add copies of the subgraph depending on the number of iterables
      iterable_params = expand_iterables(iterables, synchronize)
      # If there are no iterable subgraphs, then return
      if not iterable_params:
          return supergraph
      # Make an iterable subgraph node id template
      count = len(iterable_params)
      template = ".%s%%0%dd" % (prefix, np.ceil(np.log10(count)))
      # Copy the iterable subgraphs
      for i, params in enumerate(iterable_params):
          Gc = deepcopy(subgraph)
          ids = [n._hierarchy + n._id for n in Gc.nodes()]
          nodeidx = ids.index(nodeid)
          rootnode = list(Gc.nodes())[nodeidx]
          paramstr = ""
          for key, val in sorted(params.items()):
              paramstr = "{}_{}_{}".format(
                  paramstr,
                  _get_valid_pathstr(key),
                  _get_valid_pathstr(val),
              )
              rootnode.set_input(key, val)
  
          logger.debug("Parameterization: paramstr=%s", paramstr)
          levels = get_levels(Gc)
          for n in Gc.nodes():
              # update parameterization of the node to reflect the location of
              # the output directory.  For example, if the iterables along a
              # path of the directed graph consisted of the variables 'a' and
              # 'b', then every node in the path including and after the node
              # with iterable 'b' will be placed in a directory
              # _a_aval/_b_bval/.
  
              path_length = levels[n]
              # enter as negative numbers so that earlier iterables with longer
              # path lengths get precedence in a sort
              paramlist = [(-path_length, paramstr)]
              if n.parameterization:
                  n.parameterization = paramlist + n.parameterization
              else:
                  n.parameterization = paramlist
          supergraph.add_nodes_from(Gc.nodes())
          supergraph.add_edges_from(Gc.edges(data=True))
          for node in Gc.nodes():
              if node._hierarchy + node._id in list(edgeinfo.keys()):
                  for info in edgeinfo[node._hierarchy + node._id]:
                      supergraph.add_edges_from(
                          [(info[0], node, info[1])]
                      )
              node._id += template % i
      return supergraph
  
imports: import os
  import sys
  import pickle
  from collections import defaultdict
  import re
  from copy import deepcopy
  from glob import glob
  from pathlib import Path
  from traceback import format_exception
  from hashlib import sha1
  from functools import reduce
  import numpy
  from ...None import logging, config
  from ...utils.filemanip import indirectory, relpath, fname_presuffix, ensure_list, get_related_files, save_json, savepkl, loadpkl, write_rst_header, write_rst_dict, write_rst_list
  from ...utils.misc import str2bool
  from ...utils.functions import create_function_from_source
  from ...interfaces.base.traits_extension import rebase_path_traits, resolve_path_traits, OutputMultiPath, isdefined, Undefined
  from ...interfaces.base.support import Bunch, InterfaceResult
  from ...interfaces.base import CommandLine
  from ...interfaces.utility import IdentityInterface
  from ...utils.provenance import ProvStore, pm, nipype_ns, get_id
  from inspect import signature
label_desc: Use image node_id 23 for calling a global function with example usage: _merge_graphs(supergraph, nodes, subgraph, nodeid, iterables, prefix, synchronize) and returns: supergraph, supergraph