code: def _install_necessary_subdatasets(
      ds, path, reckless, refds_path, description=None
  ):
      """Installs subdatasets of `ds`, that are necessary to obtain in order
      to have access to `path`.
  
      Gets the subdataset containing `path` regardless of whether or not it was
      already installed. While doing so, installs everything necessary in between
      the uppermost installed one and `path`.
  
      Note: `ds` itself has to be installed.
  
      Parameters
      ----------
      ds: Dataset
      path: str
      reckless: bool
      """
      # figuring out what dataset to start with, --contains limits --recursive
      # to visit only subdataset on the trajectory to the target path
      subds_trail = ds.subdatasets(
          contains=path,
          recursive=True,
          on_failure="ignore",
          result_filter=is_ok_dataset,
          result_renderer="disabled",
      )
      if not subds_trail:
          # there is not a single known subdataset (installed or not)
          # for this path -- job done
          return
      # otherwise we start with the one deepest down
      cur_subds = subds_trail[-1]
  
      while not GitRepo.is_valid_repo(cur_subds["path"]):
          # install using helper that give some flexibility regarding where to
          # get the module from
          for res in _install_subds_from_flexible_source(
              Dataset(cur_subds["parentds"]),
              cur_subds,
              reckless=reckless,
              description=description,
          ):
              if res.get("action", None) == "install":
                  if res["status"] == "ok":
                      # report installation, whether it helped or not
                      res["message"] = (
                          "Installed subdataset in order to get %s",
                          str(path),
                      )
                      # next subdataset candidate
                      sd = Dataset(res["path"])
                      yield res
                  elif res["status"] in ("impossible", "error"):
                      yield res
                      # we cannot go deeper, we need to stop
                      return
                  else:
                      # report unconditionally to caller
                      yield res
          if sd.pathobj == path:
              # we've just got the target subdataset, we're done
              return
          # now check whether the just installed subds brought us any closer to
          # the target path
          subds_trail = sd.subdatasets(
              contains=path,
              recursive=False,
              on_failure="ignore",
              result_filter=is_ok_dataset,
              result_renderer="disabled",
          )
          if not subds_trail:
              # no (newly available) subdataset gets us any closer
              return
          # next round
          cur_subds = subds_trail[-1]
  
imports: import logging
  import re
  import os.path
  from datalad.config import ConfigManager
  from datalad.interface.base import Interface, eval_results
  from datalad.interface.base import build_doc
  from datalad.interface.results import get_status_dict, results_from_paths, annexjson2result, success_status_map, results_from_annex_noinfo
  from datalad.interface.common_opts import recursion_flag, location_description, jobs_opt, reckless_opt
  from datalad.interface.results import is_ok_dataset
  from datalad.support.constraints import EnsureInt, EnsureChoice, EnsureStr, EnsureNone
  from datalad.support.collections import ReadOnlyDict
  from datalad.support.param import Parameter
  from datalad.support.annexrepo import AnnexRepo
  from datalad.support.gitrepo import GitRepo, _fixup_submodule_dotgit_setup
  from datalad.support.exceptions import CapturedException, CommandError, InsufficientArgumentsError
  from datalad.support.network import URL, RI, urlquote
  from datalad.support.parallel import ProducerConsumerProgressLog
  from datalad.utils import unique, Path, get_dataset_root, shortened_repr
  from datalad.local.subdatasets import Subdatasets
  from datalad.distribution.dataset import Dataset, EnsureDataset, datasetmethod, require_dataset
  from datalad.core.distributed.clone import clone_dataset
  from datalad.distribution.utils import _get_flexible_source_candidates
label_desc: Use image node_id 4 for calling a global function with example usage: _install_necessary_subdatasets(ds, path, reckless, refds_path, description) without return types