Muhammad Saeed

Hi @mohame54 , thanks for the PR. lgtm.

Hi @Adeola23, thanks for your PR. The implementation seems both accurate and efficient. However, you only seem to be testing for very limited tensor shapes. Could you please change it...

This makes sense. The reason why this happens is that GLU halves the dimension/axis of interest; therefore, it must be even. For example, given an input x with shape [3,...

Hi @AkshayVenkataraman, thanks for the PR. There's a conflict in `test_frontends.test_paddl.test_tensor.test_paddle_tensor.py`. Additionally, The testing functions are not sufficient for the following args: Axis and num_dims need to be tested further....

Thanks for your modifications. axis, num_dims, and available_dtypes tests are now more exhaustive; but there's still a merge conflict in the following two files: `ivy/functional/frontends/paddle/tensor/math.py` `ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_math.py`

lgtm.

Hi @MuhammadNizamani, thanks for pointing out this issue. Indeed, there's a problem with the test function that wasn't showing up. Unfortunately, there's some conflict issues, so I'm unable to revert...

Hi @rullo16, thanks a lot for your PR. Apologies for not getting to you earlier. Everything looks good. Just some minor modifications. Since TensorFlow supports `float16` for `log_poission_loss`, there seems...

Hi @rullo16, lgtm.

Hi @Adeola23, the `.filter(lambda x: x[-1] % 2 == 0)` seems to do the trick! However, you only seem to be testing for the last dimension (i.e. axis = -1).