`with_format` behavior is inconsistent on different datasets
Author: fxmarty
State: open
Labels: name: bug

## Describe the bug
I found a case where `with_format` does not transform the dataset to the requested format.

## Steps to reproduce the bug
Run:

```python
from transformers import AutoTokenizer, AutoFeatureExtractor
from datasets import load_dataset

raw = load_dataset("glue", "sst2", split="train")
raw = raw.select(range(100))

tokenizer = AutoTokenizer.from_pretrained("philschmid/tiny-bert-sst2-distilled")

def preprocess_func(examples):
    return tokenizer(examples["sentence"], padding=True, max_length=256, truncation=True)

data = raw.map(preprocess_func, batched=True)

print(type(data[0]["input_ids"]))

data = data.with_format("torch", columns=["input_ids"])

print(type(data[0]["input_ids"]))
```

printing as expected:

```python
<class 'list'>
<class 'torch.Tensor'>
```

Then run:

```python
raw = load_dataset("beans", split="train")
raw = raw.select(range(100))

preprocessor = AutoFeatureExtractor.from_pretrained("nateraw/vit-base-beans")

def preprocess_func(examples):
    imgs = [img.convert("RGB") for img in examples["image"]]
    return preprocessor(imgs)

data = raw.map(preprocess_func, batched=True)

print(type(data[0]["pixel_values"]))

data = data.with_format("torch", columns=["pixel_values"])

print(type(data[0]["pixel_values"]))
```

Printing, unexpectedly

```python
<class 'list'>
<class 'list'>
```

## Expected results
`with_format` should transform into the requested format; it's not the case.

## Actual results
`type(data[0]["pixel_values"])` should be `torch.Tensor` in the example above

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: dev version, commit 44af3fafb527302282f6b6507b952de7435f0979
- Platform: Linux
- Python version: 3.9.12
- PyArrow version: 7.0.0


Comments: Hi! You can get a `torch.Tensor` if you do the following:
```python
raw = load_dataset("beans", split="train")
raw = raw.select(range(100))

preprocessor = AutoFeatureExtractor.from_pretrained("nateraw/vit-base-beans")

from datasets import Array3D
features = raw.features.copy()
features["pixel_values"] = datasets.Array3D(shape=(3, 224, 224), dtype="float32")

def preprocess_func(examples):
    imgs = [img.convert("RGB") for img in examples["image"]]
    return preprocessor(imgs)

data = raw.map(preprocess_func, batched=True, features=features)

print(type(data[0]["pixel_values"]))

data = data.with_format("torch", columns=["pixel_values"])

print(type(data[0]["pixel_values"]))
```

The reason for this "inconsistency" in the default case is the way PyArrow infers the type of multi-dim arrays (in this case, the `pixel_values` column). If the type is not specified manually, PyArrow assumes it is a dynamic-length sequence (it needs to know the type before writing the first batch to a cache file, and it can't be sure the array is fixed ahead of time; `ArrayXD` is our way of telling that the dims are fixed), so it already fails to convert the corresponding array to NumPy properly (you get an array of `np.object` arrays). And `with_format("torch")` replaces NumPy arrays with Torch tensors, so this bad formatting propagates.