code: def _load(original_fn, f, *args, **kwargs):
      if not PatchedJoblib._current_task:
          return original_fn(f, *args, **kwargs)
  
      filename = get_filename_from_file_object(f, flush=False)
  
      # register input model
      empty = _Empty()
      # Hack: disabled
      if False and running_remotely():
          # we assume scikit-learn, for the time being
          current_framework = Framework.scikitlearn
          filename = WeightsFileHandler.restore_weights_file(
              empty,
              filename,
              current_framework,
              PatchedJoblib._current_task,
          )
          model = original_fn(filename or f, *args, **kwargs)
      else:
          # try to load model before registering, in case we fail
          model = original_fn(f, *args, **kwargs)
          current_framework = PatchedJoblib.get_model_framework(model)
          WeightsFileHandler.restore_weights_file(
              empty,
              filename,
              current_framework,
              PatchedJoblib._current_task,
          )
  
      if empty.trains_in_model:
          # noinspection PyBroadException
          try:
              model.trains_in_model = empty.trains_in_model
          except Exception:
              pass
      return model
  
imports: import sys
  import warnings
  from functools import partial
  import six
  from pathlib2 import Path
  from .import_bind import PostImportHookPatching
  from ..binding.frameworks import _patched_call, _Empty, WeightsFileHandler
  from ..config import running_remotely
  from ..debugging.log import LoggerRoot
  from ..model import Framework
  from ..utilities.lowlevel.file_access import get_filename_from_file_object, buffer_writer_close_cb
label_desc: Use image node_id 8 for calling the PatchedJoblib obj's underlying member method code with example usage: obj._load(original_fn, f) and returns: model, original_fn