python multiprocessing : provide one specific argument to each worker

I'm using a Pool of workers and want each of them to get initialized with a specific object. More precisely, the initialization cannot be parallelized, so that I plan to prepare the objects in the main process before to create the workers and give each worker one of these objects.

Here is my attempt :

import multiprocessing
import random
import time

class Foo:
    def __init__(self, param):
        # NO WAY TO PARALLELIZE THIS !!
        print(f"Creating Foo with {param}")
        self.param = param

    def __call__(self, x):
        time.sleep(1)
        print("Do the computation", self)
        return self.param + str(x)

def initializer():
    global myfoo

    param = random.choice(["a", "b", "c", "d", "e"])
    myfoo = Foo(param)

def compute(x):
    return myfoo(x)

multiple_results = []
with multiprocessing.Pool(2, initializer, ()) as pool:
    for i in range(1, 10):
        work = pool.apply_async(compute, (i,))
        multiple_results.append(work)

    print([res.get(timeout=2) for res in multiple_results])

Here is a possible output:

Creating Foo with b
Creating Foo with a
Do the computation <__main__.Foo object at 0x7f8d70aa7fd0>
Do the computation <__main__.Foo object at 0x7f8d70aa7fd0>
Do the computation <__main__.Foo object at 0x7f8d70aa7fd0>
Do the computation <__main__.Foo object at 0x7f8d70aa7fd0>
Do the computation <__main__.Foo object at 0x7f8d70aa7fd0>
Do the computation <__main__.Foo object at 0x7f8d70aa7fd0>
Do the computation <__main__.Foo object at 0x7f8d70aa7fd0>
Do the computation <__main__.Foo object at 0x7f8d70aa7fd0>
Do the computation <__main__.Foo object at 0x7f8d70aa7fd0>
['b1', 'a2', 'b3', 'a4', 'b5', 'a6', 'b7', 'a8', 'b9']

What is puzzling me is that the address of the Foo object is always the same while the actual Foo object is different as can be seen by the output: "b1", "a2".

My problem is that the two calls to initializer are parallelized while I do not want to parallelize the construction of Foo.

I want some magical method add_worker to do something like this:

pool = multiprocessing.Pool()
for i in range(0,2):
    foo = Foo()
    poo.add_worker(initializer, (foo,))

Any ideas ?

EDIT: I solved my real live problem by making the import of kera's VGGNet inside the process instead of on top of the file. See this answer For the sake of curiosity, I remain interested in an answer.