IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number
### Describe the bug

When dataset contains a 0-dim tensor, formatting.py raises a following error and fails.
```bash
Traceback (most recent call last):
  File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 501, in format_row
    return _unnest(formatted_batch)
  File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in _unnest
    return {key: array[0] for key, array in py_dict.items()}
  File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in <dictcomp>
    return {key: array[0] for key, array in py_dict.items()}
IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number
```

### Steps to reproduce the bug

Load whichever dataset and add transform method to add 0-dim tensor. Or create/find a dataset containing 0-dim tensor. E.g.

```python
from datasets import load_dataset
import torch

dataset = load_dataset("lambdalabs/pokemon-blip-captions", split='train')
def t(batch):
    return {"test": torch.tensor(1)}
    
dataset.set_transform(t)
d_0 = dataset[0]
```

### Expected behavior

Extractor will correctly get a row from the dataset, even if it contains 0-dim tensor.

### Environment info

`datasets==2.8.0`, but it looks like it is also applicable to main branch version (as of 16th February)
Hi! The `set_transform` does not apply a custom formatting transform on a single example but the entire batch, so the fixed version of your transform would look as follows:
```python
from datasets import load_dataset
import torch

dataset = load_dataset("lambdalabs/pokemon-blip-captions", split='train')
def t(batch):
    return {"test": torch.tensor([1] * len(batch[next(iter(batch))]))}
    
dataset.set_transform(t)
d_0 = dataset[0]
```

Still, the formatter's error message should mention that a dict of **sequences** is expected as the returned value (not just a dict) to make debugging easier. I can take this Fixed in #5553  > Hi! The `set_transform` does not apply a custom formatting transform on a single example but the entire batch, so the fixed version of your transform would look as follows:
> 
> ```python
> from datasets import load_dataset
> import torch
> 
> dataset = load_dataset("lambdalabs/pokemon-blip-captions", split='train')
> def t(batch):
>     return {"test": torch.tensor([1] * len(batch[next(iter(batch))]))}
>     
> dataset.set_transform(t)
> d_0 = dataset[0]
> ```
> 
> Still, the formatter's error message should mention that a dict of **sequences** is expected as the returned value (not just a dict) to make debugging easier.

ok, will change it according to suggestion. Thanks for the reply!