code: def load(filename, mmap_mode=None):
      """Reconstruct a Python object from a file persisted with load.
  
      Parameters
      -----------
      filename: string
          The name of the file from which to load the object
      mmap_mode: {None, 'r+', 'r', 'w+', 'c'}, optional
          If not None, the arrays are memory-mapped from the disk. This
          mode has not effect for compressed files. Note that in this
          case the reconstructed object might not longer match exactly
          the originally pickled object.
  
      Returns
      -------
      result: any Python object
          The object stored in the file.
  
      See Also
      --------
      dump : function to save an object
  
      Notes
      -----
  
      This function can load numpy array files saved separately during the
      dump. If the mmap_mode argument is given, it is passed to np.load and
      arrays are loaded as memmaps. As a consequence, the reconstructed
      object might not match the original pickled object. Note that if the
      file was saved with compression, the arrays cannot be memmaped.
      """
      file_handle = open(filename, "rb")
      # We are careful to open the file handle early and keep it open to
      # avoid race-conditions on renames. That said, if data are stored in
      # companion files, moving the directory will create a race when
      # trying to access the companion files.
      if _read_magic(file_handle) == _ZFILE_PREFIX:
          if mmap_mode is not None:
              warnings.warn(
                  'file "%(filename)s" appears to be a zip, '
                  'ignoring mmap_mode "%(mmap_mode)s" flag passed'
                  % locals(),
                  Warning,
                  stacklevel=2,
              )
          unpickler = ZipNumpyUnpickler(
              filename, file_handle=file_handle
          )
      else:
          unpickler = NumpyUnpickler(
              filename, file_handle=file_handle, mmap_mode=mmap_mode
          )
  
      try:
          obj = unpickler.load()
      finally:
          if hasattr(unpickler, "file_handle"):
              unpickler.file_handle.close()
      return obj
  
imports: import traceback
  import os
  import zlib
  import warnings
  import dill
  import pickle
  from io import BytesIO
label_desc: Use image node_id 6 for calling a global function with example usage: load(filename, mmap_mode) and returns: obj