Save nparray as list
NONE
In addition, when I use `set_format ` and index the ds, the following error occurs:
the code
```python
ds.set_format(type="np", colums="pixel_values")
```
error
<img width="918" alt="image" src="https://github.com/user-attachments/assets/b28bbff2-20ea-4d28-ab62-b4ed2d944996">

2024-07-15T12:20:48Z
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>  Some people use the set_format function to convert the column back, but doesn't this lose precision?

Under the hood the data is saved in Arrow format using the same precision as your numpy arrays?
By default the Arrow data is read as python lists, but you can indeed read them back as numpy arrays with the same precision
2024-07-17T12:42:09Z
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(you can fix your second issue by fixing the typo `colums` -> `columns`)
2024-07-17T12:43:10Z
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> (you can fix your second issue by fixing the typo `colums` -> `columns`)

You are right, I was careless. Thank you.
2024-07-18T11:31:55Z
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> > Some people use the set_format function to convert the column back, but doesn't this lose precision?
> 
> Under the hood the data is saved in Arrow format using the same precision as your numpy arrays? By default the Arrow data is read as python lists, but you can indeed read them back as numpy arrays with the same precision

Yes, after testing I found that there was no loss of precision. Thanks again for your answer.
2024-07-18T11:33:25Z
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### Describe the bug

When I use the `map` function to convert images into features, datasets saves nparray as a list. Some people use the `set_format` function to convert the column back, but doesn't this lose precision?

### Steps to reproduce the bug

the map function
```python
def convert_image_to_features(inst, processor, image_dir):
    image_file = inst["image_url"]
    file = image_file.split("/")[-1]
    image_path = os.path.join(image_dir, file)
    image = Image.open(image_path)
    image = image.convert("RGBA")

    inst["pixel_values"] = processor(images=image, return_tensors="np")["pixel_values"]
    return inst
```

main function
```python
map_fun = partial(
      convert_image_to_features, processor=processor, image_dir=image_dir
  )
ds = ds.map(map_fun, batched=False, num_proc=20)
print(type(ds[0]["pixel_values"])
```

### Expected behavior

(type < list>) 

### Environment info

- `datasets` version: 2.16.1
- Platform: Linux-4.19.91-009.ali4000.alios7.x86_64-x86_64-with-glibc2.35
- Python version: 3.11.5
- `huggingface_hub` version: 0.23.4
- PyArrow version: 14.0.2
- Pandas version: 2.1.4
- `fsspec` version: 2023.10.0