# Buggy code
def get_subjects_iterable(self) -> Iterator:
    # I need a DataLoader to handle parallelism
    # But this loader is always expected to yield single subject samples
    self._print("\nCreating subjects loader with", self.num_workers, "workers")
    subjects_loader = DataLoader(
        self.subjects_dataset,
        num_workers=self.num_workers,
        collate_fn=lambda x: x[0],
        shuffle=self.shuffle_subjects,
    )
    return iter(subjects_loader)


# Traceback (AttributeError)
Traceback (most recent call last):
File "C:\Users\fzj\anaconda3\lib\multiprocessing\popen_spawn_win32.py", line 93, in __init__
reduction.dump(process_obj, to_child)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
AttributeError: Can't pickle local object 'Queue.get_subjects_iterable.<locals>.<lambda>'
python-BaseException
Traceback (most recent call last):
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 125, in _main
prepare(preparation_data)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "C:\Users\fzj\anaconda3\lib\runpy.py", line 265, in run_path
return _run_module_code(code, init_globals, run_name,
File "C:\Users\fzj\anaconda3\lib\runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "C:\Users\fzj\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\fzj\Google Drive\PHD project\3_src\DL\sli_net\main_loader.py", line 10, in <module>
patches_queue = tio.Queue(
File "C:\Users\fzj\anaconda3\lib\site-packages\torchio\data\queue.py", line 146, in __init__
self.subjects_iterable = self.get_subjects_iterable()
File "C:\Users\fzj\anaconda3\lib\site-packages\torchio\data\queue.py", line 240, in get_subjects_iterable
return iter(subjects_loader)
File "C:\Users\fzj\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 352, in __iter__
return self._get_iterator()
File "C:\Users\fzj\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 294, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "C:\Users\fzj\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 801, in __init__
w.start()
File "C:\Users\fzj\anaconda3\lib\multiprocessing\process.py", line 121, in start
self._popen = self._Popen(self)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\context.py", line 326, in _Popen
return Popen(process_obj)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\popen_spawn_win32.py", line 45, in __init__
prep_data = spawn.get_preparation_data(process_obj._name)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 154, in get_preparation_data
_check_not_importing_main()
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 134, in _check_not_importing_main
raise RuntimeError('''
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.

This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:

if __name__ == '__main__':
freeze_support()
...

The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
python-BaseException

Process finished with exit code -1

# Fixed code
def get_subjects_iterable(self) -> Iterator:
    # I need a DataLoader to handle parallelism
    # But this loader is always expected to yield single subject samples
    self._print(f"\nCreating subjects loader with {self.num_workers} workers")
    subjects_loader = DataLoader(
        self.subjects_dataset,
        num_workers=self.num_workers,
        pin_memory=self.pin_memory,
        batch_size=1,
        collate_fn=self.get_first_item,
        shuffle=self.shuffle_subjects,
    )
    return iter(subjects_loader)