Fix Frontend Failing Test: tensorflow - mathematical_functions.jax.numpy.sinc

[ZJay07]

PR Description

bfloat16 seems to be not supported in the tensorflow backend for sinc

Related Issue

Close #28676

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?

Socials

[github-actions]

PR Compliance Checks

Thank you for your Pull Request! We have run several checks on this pull request in order to make sure it's suitable for merging into this project. The results are listed in the following section.

Issue Reference

In order to be considered for merging, the pull request description must refer to a specific issue number. This is described in our contributing guide and our PR template.
This check is looking for a phrase similar to: "Fixes #XYZ" or "Resolves #XYZ" where XYZ is the issue number that this PR is meant to address.

Conventional Commit PR Title

In order to be considered for merging, the pull request title must match the specification in conventional commits. You can edit the title in order for this check to pass.
Most often, our PR titles are something like one of these:

• docs: correct typo in README
• feat: implement dark mode"
• fix: correct remove button behavior

Linting Errors

• Found type "null", must be one of "feat","fix","docs","style","refactor","perf","test","build","ci","chore","revert"
• No subject found

[Ishticode]

Thank you very much @ZJay07 for the PR
You are right in recognizing that ivy.sinc with tf backend throws an error but this looks like an ivy issue. Becaue the code in the actual implementation works with native tensorflow as in

import tensorflow as tf
x = tf.constant([1, 2, 3.], dtype=tf.bfloat16)
x = x*3.14
print(tf.cast(tf.where(x == 0, 1, tf.math.sin(x) / x), x.dtype))

which means it does support bfloat16 natively.
Are you able to figure out where our code creates an issue

[ZJay07]

Hi @Ishticode, it should work now, I'm using the tf version of sinc and cast it back to the same dtype of x which follows the characteristic of jax.numpy.sinc which mentions out – sinc(x), which has the same shape as the input.

ref: https://jax.readthedocs.io/en/latest/_autosummary/jax.numpy.sinc.html

[Ishticode]

seems like the right approach but now another test is failed due to this change

This is because the change is made in tensorflow backend, and when those two tests are ran with tensorflow backends they get value mismatches as you can see under "Combined Test Results".
NOTE: You will find the logs at https://github.com/unifyai/ivy/actions/runs/8513118977/job/23316369932?pr=28677.

[ZJay07]

Seems like ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py::test_torch_sinc_,tensorflow is still failing, I'll work on it

[Ishticode]

sure

[Ishticode]

any updates @ZJay07 :)