Streaming IterableDatasets do not work with torch DataLoaders
### Describe the bug

When using streaming datasets set up with train/val split using `.skip()` and `.take()`, the following error occurs when iterating over a torch dataloader:
```
  File "/Users/julian/miniconda3/envs/sims/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 363, in __iter__
    self._iterator = self._get_iterator()
  File "/Users/julian/miniconda3/envs/sims/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 314, in _get_iterator
    return _MultiProcessingDataLoaderIter(self)
  File "/Users/julian/miniconda3/envs/sims/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 927, in __init__
    w.start()
  File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/process.py", line 121, in start
    self._popen = self._Popen(self)
  File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/context.py", line 224, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/context.py", line 284, in _Popen
    return Popen(process_obj)
  File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 32, in __init__
    super().__init__(process_obj)
  File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/popen_fork.py", line 19, in __init__
    self._launch(process_obj)
  File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 47, in _launch
    reduction.dump(process_obj, fp)
  File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj)
AttributeError: Can't pickle local object '_generate_examples_from_tables_wrapper.<locals>.wrapper'
```

To reproduce, run the code 
```
from datasets import load_dataset
data = load_dataset(args.dataset_name, split="train", streaming=True)
train_len = 5000
val_len = 100

train, val = data.take(train_len), data.skip(train_len).take(val_len)
traindata = IterableClipDataset(data, context_length=args.max_len, tokenizer=tokenizer, image_key="url", text_key="text")

traindata = DataLoader(traindata, batch_size=args.batch_size, num_workers=args.num_workers, persistent_workers=True)
```

Where the class IterableClipDataset is a simple wrapper to cast the dataset to a torch iterabledataset, defined via 
```
from torch.utils.data import Dataset, IterableDataset
from torchvision.transforms import Compose, Resize, ToTensor
from transformers import AutoTokenizer
import requests
from PIL import Image

class IterableClipDataset(IterableDataset):
    def __init__(self, dataset, context_length: int, image_transform=None, tokenizer=None, image_key="image", text_key="text"):
        self.dataset = dataset
        self.context_length = context_length
        self.image_transform = Compose([Resize((224, 224)), ToTensor()]) if image_transform is None else image_transform
        self.tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") if tokenizer is None else tokenizer
        self.image_key = image_key
        self.text_key = text_key

    def read_image(self, url: str):
        try: # Try to read the image
            image = Image.open(requests.get(url, stream=True).raw)
        except:
            image = Image.new("RGB", (224, 224), (0, 0, 0))
        return image
    
    def process_sample(self, image, text):
        if isinstance(image, str):
            image = self.read_image(image)
        if self.image_transform is not None:
            image = self.image_transform(image)
        text = self.tokenizer.encode(
            text, add_special_tokens=True, max_length=self.context_length, truncation=True, padding="max_length"
        )
        text = torch.tensor(text, dtype=torch.long)
        return image, text

    def __iter__(self):
        for sample in self.dataset:
            image, text = sample[self.image_key], sample[self.text_key]
            yield self.process_sample(image, text)
```



### Steps to reproduce the bug

Steps to reproduce
1. Install `datasets`, `torch`, and `PIL` (if you want to reproduce exactly)
2. Run the code above

### Expected behavior

Batched data is produced from the dataloader 

### Environment info

```
datasets == 2.9.0
python == 3.9.12
torch == 1.11.0
```