RuntimeError: context has already been set - Finetuning deepdoctection

Hello,
I am trying to fine-tune deepdoctection tensorpack on fintabnet dataset. I followed instructions on fine-tune notebook but I got this error :
RuntimeError Traceback (most recent call last)
Cell In[140], line 1
----> 1 temp = dd.train_faster_rcnn(path_config_yaml=path_config_yaml,
2 dataset_train=fintabnet,
3 path_weights=path_weights,
4 config_overwrite=config_overwrite,
5 log_dir="/kaggle/working/logs/",
6 build_train_config=build_train_config,
7 dataset_val=dataset_val,
8 build_val_config=build_val_config,
9 metric_name="coco",
10 pipeline_component_name="ImageLayoutService"
11 )
File /kaggle/working/deepdoctection/deepdoctection/train/tp_frcnn_train.py:253, in train_faster_rcnn(path_config_yaml, dataset_train, path_weights, config_overwrite, log_dir, build_train_config, dataset_val, build_val_config, metric_name, metric, pipeline_component_name)
File /kaggle/working/deepdoctection/deepdoctection/train/tp_frcnn_train.py:136, in get_train_dataflow(dataset, config, use_multi_proc_for_train, **build_train_kwargs)
File /kaggle/working/deepdoctection/deepdoctection/utils/file_utils.py:657, in set_mp_spawn()
655 if not _S.mp_context_set:
656 _S.freeze(False)
--> 657 mp.set_start_method("spawn")
658 _S.mp_context_set = True
659 _S.freeze()
File /opt/conda/lib/python3.10/multiprocessing/context.py:247, in DefaultContext.set_start_method(self, method, force)
245 def set_start_method(self, method, force=False):
246 if self._actual_context is not None and not force:
--> 247 raise RuntimeError('context has already been set')
248 if method is None and force:
249 self._actual_context = None
RuntimeError: context has already been set

My code :
temp = dd.train_faster_rcnn(path_config_yaml=path_config_yaml,
dataset_train=fintabnet,
path_weights=path_weights,
config_overwrite=config_overwrite,
log_dir="/kaggle/working/logs/",
build_train_config=build_train_config,
dataset_val=dataset_val,
build_val_config=build_val_config,
metric_name="coco",
pipeline_component_name="ImageLayoutService"
)

Log :
log-3.log

Start with deepdoctection/utils/file_utils.py at set_mp_spawn, then trace its call from get_train_dataflow and train_faster_rcnn in deepdoctection/train/tp_frcnn_train.py. Reproduce the fine-tuning notebook call and review log-3.log alongside the traceback. Done means the reported fine-tuning call completes without the context RuntimeError.