Loading MNIST as Pytorch Dataset

## Describe the bug
Conversion of MNIST dataset to pytorch fails with bug

## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mnist", split="train")
dataset.set_format('torch')
dataset[0]
print()
```

## Expected results
Expect to see torch tensors image and label

## Actual results
Traceback (most recent call last):
  File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\pydevd.py", line 1491, in _exec
    pydev_imports.execfile(file, globals, locals)  # execute the script
  File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "C:/Users/chapm/PycharmProjects/multiviewdata/multiviewdata/huggingface/mnist.py", line 13, in <module>
    dataset[0]
  File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2154, in __getitem__
    return self._getitem(
  File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2139, in _getitem
    formatted_output = format_table(
  File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 532, in format_table
    return formatter(pa_table, query_type=query_type)
  File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 281, in __call__
    return self.format_row(pa_table)
  File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 58, in format_row
    return self.recursive_tensorize(row)
  File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 54, in recursive_tensorize
    return map_nested(self._recursive_tensorize, data_struct, map_list=False)
  File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 356, in map_nested
    mapped = [
  File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 357, in <listcomp>
    _single_map_nested((function, obj, types, None, True, None))
  File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in _single_map_nested
    return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
  File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in <dictcomp>
    return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
  File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 293, in _single_map_nested
    return function(data_struct)
  File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 51, in _recursive_tensorize
    return self._tensorize(data_struct)
  File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 38, in _tensorize
    if np.issubdtype(value.dtype, np.integer):
AttributeError: 'bytes' object has no attribute 'dtype'
python-BaseException


## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Windows-10-10.0.22579-SP0
- Python version: 3.9.2
- PyArrow version: 8.0.0
- Pandas version: 1.4.1



Hi ! We haven't implemented the conversion from images data to PyTorch tensors yet I think

cc @mariosasko 

So I understand:

set_format() does not properly do the conversion to pytorch tensors from PIL images.

So that someone who stumbles on this can use the package:

```python
dataset = load_dataset("mnist", split="train")
def transform_func(examples):
    examples["image"] = [np.array(img) for img in examples["image"]]
    return examples
dataset = dataset.with_transform(transform_func)
dataset[0]
``` 

This then appears to work with pytorch dataloaders as:
```
dataloader=torch.utils.data.DataLoader(dataset,batch_size=1)
```

and tensorflow as:
```
dataset=dataset.to_tf_dataset(batch_size=1)
```

Hi! `set_transform`/`with_transform` is indeed the correct solution for the conversion. Improving this part of the API is one of the things I'm working on currently, so stay tuned!