Multiprocessing with `Dataset.map` and  `PyTorch` results in deadlock 
## Describe the bug
There seems to be an issue with using multiprocessing with `datasets.Dataset.map` (i.e. setting `num_proc` to a value greater than one) combined with a function that uses `torch` under the hood.  The subprocesses that `datasets.Dataset.map` spawns [a this step](https://github.com/huggingface/datasets/blob/1b935dab9d2f171a8c6294269421fe967eb55e34/src/datasets/arrow_dataset.py#L2663) go into wait mode forever.  

## Steps to reproduce the bug
The below code goes into  deadlock when `NUMBER_OF_PROCESSES` is greater than one.
```python

NUMBER_OF_PROCESSES = 2

from transformers import AutoTokenizer, AutoModel
from datasets import load_dataset

dataset = load_dataset("glue", "mrpc", split="train")
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
model.to("cpu")

def cls_pooling(model_output):
    return model_output.last_hidden_state[:, 0]

def generate_embeddings_batched(examples):
    sentences_batch = list(examples['sentence1'])
    encoded_input = tokenizer(
        sentences_batch, padding=True, truncation=True, return_tensors="pt"
    )
    encoded_input = {k: v.to("cpu") for k, v in encoded_input.items()}
    model_output = model(**encoded_input)
    embeddings = cls_pooling(model_output)
    examples['embeddings'] = embeddings.detach().cpu().numpy() # 64, 384
    return examples

embeddings_dataset = dataset.map(
    generate_embeddings_batched, 
    batched=True,
    batch_size=10,
    num_proc=NUMBER_OF_PROCESSES
)
```
While debugging it I've seen that it gets "stuck" when calling `torch.nn.Embedding.forward` but some testing shows that the same happens with other functions from `torch.nn`. 

## Environment info
- Platform: Linux-5.14.0-1052-oem-x86_64-with-glibc2.31
- Python version: 3.9.14
- PyArrow version: 9.0.0
- Pandas version: 1.5.0

Not sure if this is a HF problem, a PyTorch problem or something I'm doing wrong..

Thanks!
