fix: Fixed stat.paddle.mean for all backends

[Sai-Suraj-27]

PR Description

Fixed stat.paddle.mean for all backends

Related Issue

Closes #28082
Closes #28083
Closes #28084
Closes #28085
Closes #28086

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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[Sarvesh-Kesharwani]

please work on fixing these test failures

[Sai-Suraj-27]

please work on fixing these test failures

Hey, @Sarvesh-Kesharwani thanks for the review. I'm not sure, how many examples you set while testing, I tested with 25, 50, 100 and 150 examples.

All, the backends are passing with 50 examples.

Although when I tested with 100 examples, only torch backend is failing and when I tested with 150 examples only numpy backend is failing.

But, it is actually not any problem with code, it's just the accuracy of the result. You can see that the test is failing due to a minute difference of 10^(-3) difference in the outputs from the 2 backends, Due to this small difference some tests are failing when we set the number of examples too high.

I guess, that is the reason, Even in the CI, the test is not shown as failed because in the CI tests are ran with only 25 examples.

So, I guess we can merge this, no need to change anything. Please let me know your opinion.
Thank you.

[KareemMAX]

These seem unnecessary

Suggested change

	input_dtype, x, axis, dtype3, where = dtype_and_x
	input_dtype, x, axis = dtype_and_x

[Sai-Suraj-27]

Hey, you can see in this logs, that is the reason I added those 2 variables.
This following function returns 5 values for the case of "mean". So, those 2 variables must be there
https://github.com/unifyai/ivy/blob/995886245e9cde9885ee02b5a4ba4668427230a2/ivy_tests/test_ivy/test_functional/test_core/test_statistical.py#L40
https://github.com/unifyai/ivy/blob/995886245e9cde9885ee02b5a4ba4668427230a2/ivy_tests/test_ivy/test_functional/test_core/test_statistical.py#L98

[KareemMAX]

Oh, I thought it won't unpack the rest like JS (sorry have been off Python for a while). But why don't we use this?

Suggested change

	input_dtype, x, axis, dtype3, where = dtype_and_x
	input_dtype, x, axis = dtype_and_x[:2]

Not sure if I'm slicing correctly though

[Sai-Suraj-27]

Done👍🏻

[KareemMAX]

Also, regarding the current issues with the failing tests. They shouldn't fail even if the code appears right, the precision issue should be handled accordingly @Sai-Suraj-27. And make sure examples cited by @Sarvesh-Kesharwani are passing

[Sai-Suraj-27]

Also, regarding the current issues with the failing tests. They shouldn't fail even if the code appears right, the precision issue should be handled accordingly @Sai-Suraj-27. And make sure examples cited by @Sarvesh-Kesharwani are passing

Hey, @KareemMAX I know precision should be handled but since the test was passing in CI also. I thought it's okay. Actually this is a clear example of this 2nd case in the docs.
I have updated the test, and tested it with 150 examples, and it is successfully passing for all the backends now.

Thanks @Sarvesh-Kesharwani for spotting this 😄.

[KareemMAX]

LGTM, waiting for @Sarvesh-Kesharwani's approval to merge

[Sai-Suraj-27]

Hey, @Sarvesh-Kesharwani. Can you please look into the PR once and merge it if you think it's good 😄.
Thank you.

[Sarvesh-Kesharwani]

Okay let me test once, then I'll merge it.

[Sarvesh-Kesharwani]

Hi @Sai-Suraj-27,
LGTM now,
Thanks for the hard work.