resolves the ValueError: Unable to avoid copy while creating an array
## Also i have done some tests on real dataset

### Tested with real datasets:
- ✅ IMDB dataset with NumPy 1.26.4 and 2.3.4
- ✅ Rotten Tomatoes dataset with NumPy 1.26.4 and 2.3.4
- ✅ Artificial datasets with ClassLabel features

### Results:
- Stratified splits work correctly in both NumPy versions
- Class balance is perfectly maintained (e.g., Rotten Tomatoes: 426:426 train, 107:107 test)
- Non-stratified splits continue to work as expected
- Backward compatibility with NumPy 1.x confirmed

Below are the RAW logs of testing - 

```
(venv) F:\Python\Machine learning\datasets>pip install "numpy<2.0"
Collecting numpy<2.0
  Using cached numpy-1.26.4-cp311-cp311-win_amd64.whl.metadata (61 kB)
Using cached numpy-1.26.4-cp311-cp311-win_amd64.whl (15.8 MB)
Installing collected packages: numpy
  Attempting uninstall: numpy
    Found existing installation: numpy 2.3.4
    Uninstalling numpy-2.3.4:
      Successfully uninstalled numpy-2.3.4
Successfully installed numpy-1.26.4

[notice] A new release of pip is available: 24.0 -> 25.3
[notice] To update, run: python.exe -m pip install --upgrade pip

(venv) F:\Python\Machine learning\datasets>python test_fix.py
NumPy version: 1.26.4
============================================================

[Test 1] Testing with IMDB dataset...
README.md: 7.81kB [00:00, 7.78MB/s]
F:\Python\Machine learning\datasets\venv\Lib\site-packages\huggingface_hub\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\Users\arjun\.cache\huggingface\hub\datasets--imdb. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.
To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development
  warnings.warn(message)
Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
train-00000-of-00001.parquet: 100%|███████████████████████████████████████████████████████████████████████████████████| 21.0M/21.0M [00:06<00:00, 3.08MB/s]
Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
test-00000-of-00001.parquet: 100%|████████████████████████████████████████████████████████████████████████████████████| 20.5M/20.5M [00:07<00:00, 2.56MB/s]
Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
unsupervised-00000-of-00001.parquet: 100%|████████████████████████████████████████████████████████████████████████████| 42.0M/42.0M [00:14<00:00, 2.84MB/s]
Generating train split: 100%|█████████████████████████████████████████████████████████████████████████████| 25000/25000 [00:00<00:00, 267643.40 examples/s]
Generating test split: 100%|██████████████████████████████████████████████████████████████████████████████| 25000/25000 [00:00<00:00, 324697.85 examples/s]
Generating unsupervised split: 100%|██████████████████████████████████████████████████████████████████████| 50000/50000 [00:00<00:00, 289202.11 examples/s]
Loaded 1000 samples
✅ IMDB SUCCESS!
DatasetDict({
    train: Dataset({
        features: ['text', 'label'],
        num_rows: 800
    })
    test: Dataset({
        features: ['text', 'label'],
        num_rows: 200
    })
})
Train class distribution: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
Test class distribution: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

============================================================

[Test 2] Testing with Rotten Tomatoes dataset...
README.md: 7.46kB [00:00, ?B/s]
F:\Python\Machine learning\datasets\venv\Lib\site-packages\huggingface_hub\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\Users\arjun\.cache\huggingface\hub\datasets--rotten_tomatoes. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.
To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development
  warnings.warn(message)
Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
train.parquet: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 699k/699k [00:00<00:00, 3.46MB/s]
Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
validation.parquet: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 90.0k/90.0k [00:00<00:00, 6.80MB/s]
Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
test.parquet: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 92.2k/92.2k [00:00<00:00, 5.85MB/s]
Generating train split: 100%|███████████████████████████████████████████████████████████████████████████████| 8530/8530 [00:00<00:00, 856082.82 examples/s]
Generating validation split: 100%|██████████████████████████████████████████████████████████████████████████| 1066/1066 [00:00<00:00, 531075.91 examples/s]
Generating test split: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 1066/1066 [00:00<?, ? examples/s]
Loaded 1066 samples
✅ Rotten Tomatoes SUCCESS!
DatasetDict({
    train: Dataset({
        features: ['text', 'label'],
        num_rows: 852
    })
    test: Dataset({
        features: ['text', 'label'],
        num_rows: 214
    })
})

Train: class_0=426, class_1=426
Test:  class_0=107, class_1=107

============================================================

[Test 3] Testing without stratification (sanity check)...
✅ Non-stratified split SUCCESS!
DatasetDict({
    train: Dataset({
        features: ['text', 'label'],
        num_rows: 80
    })
    test: Dataset({
        features: ['text', 'label'],
        num_rows: 20
    })
})

============================================================
All tests completed!

```

### Upgrading numpy for >= 2

```
(venv) F:\Python\Machine learning\datasets>pip install "numpy>=2.0"
Collecting numpy>=2.0
  Using cached numpy-2.3.4-cp311-cp311-win_amd64.whl.metadata (60 kB)
Using cached numpy-2.3.4-cp311-cp311-win_amd64.whl (13.1 MB)
Installing collected packages: numpy
  Attempting uninstall: numpy
    Found existing installation: numpy 1.26.4
    Uninstalling numpy-1.26.4:
      Successfully uninstalled numpy-1.26.4
Successfully installed numpy-2.3.4

[notice] A new release of pip is available: 24.0 -> 25.3
[notice] To update, run: python.exe -m pip install --upgrade pip

(venv) F:\Python\Machine learning\datasets>python test_fix.py
NumPy version: 2.3.4
============================================================

[Test 1] Testing with IMDB dataset...
Loaded 1000 samples
✅ IMDB SUCCESS!
DatasetDict({
    train: Dataset({
        features: ['text', 'label'],
        num_rows: 800
    })
    test: Dataset({
        features: ['text', 'label'],
        num_rows: 200
    })
})
Train class distribution: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
Test class distribution: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

============================================================

[Test 2] Testing with Rotten Tomatoes dataset...
Loaded 1066 samples
✅ Rotten Tomatoes SUCCESS!
DatasetDict({
    train: Dataset({
        features: ['text', 'label'],
        num_rows: 852
    })
    test: Dataset({
        features: ['text', 'label'],
        num_rows: 214
    })
})

Train: class_0=426, class_1=426
Test:  class_0=107, class_1=107

============================================================

[Test 3] Testing without stratification (sanity check)...
✅ Non-stratified split SUCCESS!
DatasetDict({
    train: Dataset({
        features: ['text', 'label'],
        num_rows: 80
    })
    test: Dataset({
        features: ['text', 'label'],
        num_rows: 20
    })
})

============================================================
All tests completed!
 [test_fix.py](https://github.com/user-attachments/files/23140682/test_fix.py)

here is the file I used for testing @lhoestq  The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7831). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
## Summary
Fixes #7818 

This PR resolves the `ValueError: Unable to avoid copy while creating an array` error that occurs when using `train_test_split` with `stratify_by_column` parameter in NumPy 2.0+.

## Changes
- Wrapped the stratify column array access with `np.asarray()` in `arrow_dataset.py`
- This allows NumPy 2.0 to make a copy when the Arrow array is non-contiguous in memory

## Testing
- ✅ Tested with NumPy 2.3.4 - stratified splits work correctly
- ✅ Tested with NumPy 1.26.4 - backward compatibility maintained
- ✅ Verified class balance is preserved in stratified splits
- ✅ Non-stratified splits continue to work as expected

