def METHOD_NAME(library_filename):
  """Loads a TensorFlow plugin, containing custom ops and kernels.
  Pass "library_filename" to a platform-specific mechanism for dynamically
  loading a library. The rules for determining the exact location of the
  library are platform-specific and are not documented here. When the
  library is loaded, ops and kernels registered in the library via the
  `REGISTER_*` macros are made available in the TensorFlow process. Note
  that ops with the same name as an existing op are rejected and not
  registered with the process.
  Args:
    library_filename: Path to the plugin.
      Relative or absolute filesystem path to a dynamic library file.
  Returns:
    A python module containing the Python wrappers for Ops defined in
    the plugin.
  Raises:
    RuntimeError: when unable to load the library or get the python wrappers.
  """
  lib_handle = py_tf.TF_LoadLibrary(library_filename)
  op_list_str = py_tf.TF_GetOpList(lib_handle)
  op_list = op_def_pb2.OpList()
  op_list.ParseFromString(compat.as_bytes(op_list_str))
  wrappers = py_tf.GetPythonWrappers(op_list_str)
  # Delete the library handle to release any memory held in C
  # that are no longer needed.
  py_tf.TF_DeleteLibraryHandle(lib_handle)
  # Get a unique name for the module.
  module_name = hashlib.md5(wrappers).hexdigest()
  if module_name in sys.modules:
    return sys.modules[module_name]
  module = imp.new_module(module_name)
  # pylint: disable=exec-used
  exec(wrappers, module.__dict__)
  # Stash away the library handle for making calls into the dynamic library.
  module.LIB_HANDLE = lib_handle
  # OpDefs of the list of ops defined in the library.
  module.OP_LIST = op_list
  # Allow this to be recognized by AutoGraph.
  setattr(module, '_IS_TENSORFLOW_PLUGIN', True)
  sys.modules[module_name] = module
  return module