Using shared list with pathos multiprocessing raises `digest sent was rejected` error

I am attempting to use multiprocessing for the generation of complex, unpickable, objects as per the following code snippet:

from multiprocessing import Manager
from pathos.multiprocessing import ProcessingPool

class Facility:

    def __init__(self):
        self.blocks = Manager().list()

    def __process_blocks(self, block):
        designer = block["designer"]
        apply_terrain = block["terrain"]
        block_type = self.__block_type_to_string(block["type"])
        block = designer.generate_block(block_id=block["id"],
                                            block_type=block_type,
                                            anchor=Point(float(block["anchor_x"]), float(block["anchor_y"]),
                                                         float(block["anchor_z"])),
                                            pcu_anchor=Point(float(block["pcu_x"]), float(block["pcu_y"]), 0),
                                            corridor_width=block["corridor"],
                                            jb_height=block["jb_connect_height"],
                                            min_boxes=block["min_boxes"],
                                            apply_terrain=apply_terrain)
        self.blocks.append(block)

    def design(self, apply_terrain=False):
        designer = FacilityBuilder(string_locator=self._string_locator, string_router=self._string_router,
                                   box_router=self._box_router, sorter=self._sorter,
                                   tracker_configurator=self._tracker_configurator, config=self._config)
        blocks = [block.to_dict() for index, block in self._store.get_blocks().iterrows()]
        for block in blocks:
            block["designer"] = designer
            block["terrain"] = apply_terrain

        with ProcessingPool() as pool:
            pool.map(self.__process_blocks, blocks)

(Struggling to reproduce this with simpler code so I am showing actual code)

I need to update a shareable variable so I initialise a class level variable using a multiprocessing.Manager as follows:

self.blocks = Manager().list()

This leaves me with the following error (only partial stacktrace):

  File "C:\Users\Paul.Nel\Documents\repos\autoPV\.autopv\lib\site-packages\dill\_dill.py", line 481, in load
    obj = StockUnpickler.load(self)
  File "C:\Users\Paul.Nel\AppData\Local\Programs\Python\Python39\lib\multiprocessing\managers.py", line 933, in RebuildProxy
    return func(token, serializer, incref=incref, **kwds)
  File "C:\Users\Paul.Nel\AppData\Local\Programs\Python\Python39\lib\multiprocessing\managers.py", line 783, in __init__
    self._incref()
  File "C:\Users\Paul.Nel\AppData\Local\Programs\Python\Python39\lib\multiprocessing\managers.py", line 837, in _incref
    conn = self._Client(self._token.address, authkey=self._authkey)
  File "C:\Users\Paul.Nel\AppData\Local\Programs\Python\Python39\lib\multiprocessing\connection.py", line 513, in Client
    answer_challenge(c, authkey)
  File "C:\Users\Paul.Nel\AppData\Local\Programs\Python\Python39\lib\multiprocessing\connection.py", line 764, in answer_challe
nge
    raise AuthenticationError('digest sent was rejected')
multiprocessing.context.AuthenticationError: digest sent was rejected

As last resort I tried to use python's standard ThreadPool implementation to try and circumvent the pickle issue but this has not gone well either. I have read about many similar issues but have not found the solution to this particular problem. Is the problem with dill or with the way pathos interfaces with mulitprocessing.Manager?

EDIT: So I managed to replicate this with sample code as follows:

import os
import math
from multiprocessing import Manager
from pathos.multiprocessing import ProcessingPool


class MyComplex:

    def __init__(self, x):
        self._z = x * x

    def me(self):
        return math.sqrt(self._z)


class Starter:

    def __init__(self):
        manager = Manager()
        self.my_list = manager.list()

    def _f(self, value):
        print(f"{value.me()} on {os.getpid()}")
        self.my_list.append(value.me)

    def start(self):
        names = [MyComplex(x) for x in range(100)]

        with ProcessingPool() as pool:
            pool.map(self._f, names)


if __name__ == '__main__':
    starter = Starter()
    starter.start()

The error occurs when I add self.my_list = manager.list().

So I have resolved this issue. I would still be great if someone like mmckerns or someone else with more knowledge than me on multiprocessing could comment on why this is a solution.

The issue seemed to have been that the Manager().list() was declared in __init__. The following code works without any issues:

import os
import math
from multiprocessing import Manager
from pathos.multiprocessing import ProcessingPool


class MyComplex:

    def __init__(self, x):
        self._z = x * x

    def me(self):
        return math.sqrt(self._z)


class Starter:

    def _f(self, value):
        print(f"{value.me()} on {os.getpid()}")
        return value.me()

    def start(self):
        manager = Manager()
        my_list = manager.list()
        names = [MyComplex(x) for x in range(100)]

        with ProcessingPool() as pool:
            my_list.append(pool.map(self._f, names))
        print(my_list)


if __name__ == '__main__':
    starter = Starter()
    starter.start()

Here I declare the list local to the ProcessingPool operation. I can assign the result to a class level variable afterwards if I choose.