feat: Add nanquantile function to handle NaN-aware quantile calculations

[muzakkirhussain011]

Summary

This PR introduces the nanquantile function, which extends the existing quantile calculation functionality to handle NaN values in the input data.

Changes

• Added nanquantile function that computes quantiles while ignoring NaN values.
• Utilized the existing _validate_quantile function to ensure the quantiles are within the valid range.
• Included unit tests to verify the correct behavior of the nanquantile function with various input scenarios.

Impact

The nanquantile function allows for more robust statistical analysis of datasets that contain missing or invalid entries. This enhancement ensures that our quantile calculations remain accurate and reliable even in the presence of NaN values.

Additional Notes

The implementation of nanquantile closely mirrors that of the standard quantile function but leverages NumPy's nanquantile method to ignore NaNs during computation. This addition is a valuable tool for data analysis tasks where data cleanliness cannot be guaranteed.

Releated Issues
#1529
#27497

Closes #26675

Checklist

• Did you add a function?
• Did you add the tests?
• Did you run your tests and are your tests passing?
• Did pre-commit not fail on any check?
• Did you follow the steps we provided?

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[muzakkirhussain011]

Hi @Ishticode ,

I've submitted a new pull request and would appreciate your timely review. Your feedback is crucial for the ongoing improvements and I'm looking forward to your valuable comments.

Thank you for your attention!
Best Regards

[Ishticode]

Hi. Thank you for the PR.
To add a new function, we have to implement for all backends and add the test. So adding only in numpy would not be sufficient in the case of new backend addition. Let me know if omething is unclear. Maybe also worth a look through ivy's deep dive https://unify.ai/docs/ivy/overview/deep_dive.html. Thank you