tf.linalg.logdet outputs -inf on a matrix with complex data type
@drewshark I have tried to replicate the issue reported here and faced different errors. Could you please have a look at the gist [here](https://colab.research.google.com/gist/sushreebarsa/2f80059b67d119a4ff880edf90ca9467/61249.ipynb) and let us know if I am missing something to replicate this one. Thank you! Hi, sorry that I forgot to import torch. Here is the corrected code snippet:
```
import tensorflow as tf
import torch
import numpy as np
t = np.array([[1 + 1j, 4 + 1j], [4 + 2j, 3 + 1j]])
tf_res = tf.linalg.logdet(tf.constant(t, dtype=tf.complex128))
torch_res = torch.logdet(torch.tensor(t))
print(tf_res, torch_res)
``` @SuryanarayanaY I was able to replicate the issue in TF v[2.12](https://colab.research.google.com/gist/sushreebarsa/410f0ed1fb8424d5641fe81dcae5285e/61249-tf-2-12.ipynb), TF [v2.13 ](https://colab.research.google.com/gist/sushreebarsa/1a2485c4778338e1fc8f814c0982c6c1/61249-tf-2-12.ipynb#scrollTo=cReA7k7qX5vF)and tf-[nightly](https://colab.research.google.com/gist/sushreebarsa/f683666aa23191354ea2e963192ebde3/61249-nightly.ipynb). Please have a look . Thank you!
### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

2.13.0

### Custom code

Yes

### OS platform and distribution

_No response_

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Given the following matrix:
```
t = np.array([[1 + 1j, 4 + 1j], [4 + 2j, 3 + 1j]])
```
tf.linalg.logdet outputs `-inf`, I think the expected output should be a normal value. For reference, PyTorch's torch.logdet outputs `tensor(2.6688-2.5536j, dtype=torch.complex128)`

### Standalone code to reproduce the issue

```shell
import tensorflow as tf
import numpy as np
t = np.array([[1 + 1j, 4 + 1j], [4 + 2j, 3 + 1j]])
tf_res = tf.linalg.logdet(tf.constant(t, dtype=tf.complex128))
torch_res = torch.logdet(torch.tensor(t))
print(tf_res, torch_res)
```
```


### Relevant log output

```shell
tf.Tensor(-inf, shape=(), dtype=float64) tensor(2.6688-2.5536j, dtype=torch.complex128)
```
