Multiprocessing does not work properly when using the trace module.

[rls1004]

BPO	38511
Nosy	test.py: test code

^{Note: these values reflect the state of the issue at the time it was migrated and might not reflect the current state.}

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GitHub fields:

assignee = None
closed_at = None
created_at = <Date 2019-10-18.06:54:41.871>
labels = ['type-bug', '3.9', 'OS-windows']
title = 'Multiprocessing does not work properly when using the trace module.'
updated_at = <Date 2019-10-18.11:46:53.991>
user = 'https://github.com/rls1004'

bugs.python.org fields:

activity = <Date 2019-10-18.11:46:53.991>
actor = '\xd8\xaa\xd8\xa7\xd8\xb4\xd9\x8a\xd8\xb1\xd8\xa7\xd8\xaa \xd9\x85\xd8\xb3\xd8\xa7\xd9\x86\xd8\xaf'
assignee = 'none'
closed = False
closed_date = None
closer = None
components = ['Windows']
creation = <Date 2019-10-18.06:54:41.871>
creator = 'rls1004'
dependencies = []
files = ['48667']
hgrepos = []
issue_num = 38511
keywords = []
message_count = 2.0
messages = ['354864', '354879']
nosy_count = 6.0
nosy_names = ['paul.moore', 'tim.golden', 'zach.ware', 'steve.dower', 'rls1004', '\xd8\xaa\xd8\xa7\xd8\xb4\xd9\x8a\xd8\xb1\xd8\xa7\xd8\xaa \xd9\x85\xd8\xb3\xd8\xa7\xd9\x86\xd8\xaf']
pr_nums = []
priority = 'normal'
resolution = None
stage = None
status = 'open'
superseder = None
type = 'behavior'
url = 'https://bugs.python.org/issue38511'
versions = ['Python 3.9']

[rls1004]

normal result :
$ python test.py
{'p1': 1, 'p2': 1, 'p3': 1, 'p4': 1}

run with tracing :
$ python -mtrace --trackcalls test.py
{}

It seems that the foo and save functions that multiprocess should call are not called.

[ghost]

#!/usr/bin/env python
import multiprocessing, sys, trace
from functools import partial

num_list = ['p1', 'p2', 'p3', 'p4']

def foo(name):
    print(name+'\n')
    return name
def save(result, shared):
    print('a\n')
    shared.results[result] = 1

def mm():
    manager = multiprocessing.Manager()
    shared = manager.dict()
    shared.results = dict()

    clbk = partial(save, shared=shared)

    pool = multiprocessing.Pool(processes=2)
    serial_pool = multiprocessing.Pool(1)

    for name in num_list:
        serial_pool.apply_async(foo, args=[name], callback=clbk)

    pool.close()
    pool.join()

    print(shared.results)

mm()