A check fail can be triggered in Cholesky
 The gradient computation on GPU is faster for large matrices but not for large batch dimensions when the submatrices are small. In this case it might be faster to use the  cpu 

> ` @SuryanarayanaY 
I was able to reproduce the issue on Colab using tf 2.12 and tf-nightly. Please find the gist of [2.12](https://colab.research.google.com/gist/tiruk007/a8ac5acfde8ceff7c0b28fc5e8d1941f/untitled181.ipynb) and [tf-nightly](https://colab.research.google.com/gist/tiruk007/28ba594db7ef2ef77fab1ab2521fa754/untitled182.ipynb) for reference.

Thank you! Hi @shijy16 ,

The issue got resolved now with tf-nightly(2.14.0-dev20230514) and now TF is successfully able to raise the intended Error.
Please refer to attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/b238a78a3b1e85af4cb11013966b39ba/60274.ipynb).

Please check and confirm and let us know if we can close the issue as it resolved now.

Thanks! This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further. Are you satisfied with the resolution of your issue?
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<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Bug

### Have you reproduced the bug with TF nightly?

Yes

### Source

binary

### Tensorflow Version

2.13.0-dev20230406

### Custom Code

No

### OS Platform and Distribution

Linux Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.10

### Bazel version

_No response_

### GCC/Compiler version

_No response_

### CUDA/cuDNN version

CUDA 11.8

### GPU model and memory

_No response_

### Current Behaviour?

```shell
The following code can trigger a crash in `tf.raw_ops.Cholesky` due to check-fail in the latest version of TensorFlow.
```


### Standalone code to reproduce the issue

```shell
import tensorflow as tf
with tf.device("CPU"):
    input = tf.complex(tf.random.uniform([6, 1, 0, 11, 5241981715460094077, 8], dtype=tf.float64, minval=-1024, maxval=1024),tf.random.uniform([6, 1, 0, 11, 5241981715460094077, 8], dtype=tf.float64, minval=-1024, maxval=1024))
    res = tf.raw_ops.Cholesky(
        input=input,
    )
```


### Relevant log output

```shell
2023-04-09 13:50:29.887381: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-04-09 13:50:29.932917: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-04-09 13:50:30.753764: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-04-09 13:50:32.285931: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1638] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14561 MB memory:  -> device: 0, name: Tesla V100-PCIE-16GB, pci bus id: 0000:2f:00.0, compute capability: 7.0
2023-04-09 13:50:32.321965: F tensorflow/core/framework/tensor_shape.cc:201] Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Encountered overflow when multiplying 5241981715460094077 with 8, result: -1
Aborted (core dumped)
```
</details>