A check fail can be triggered in TridiagonalMatMul
@sachinprasadhs 
I was able to reproduce the issue on Colab using TF v2.11 and tf-nightly. Please find the gist of [2.12](https://colab.research.google.com/gist/tiruk007/f1f72fe0b887bddd0598f90dabab5da2/untitled185.ipynb) and [tf-nightly]( https://colab.research.google.com/gist/tiruk007/bd93c0da07e5ae4f8221d5664f718f53/untitled186.ipynb)for reference.

Thank you ! This should not be a real issue. The check failure is an overflow due to 

```py
    superdiag = tf.random.uniform([5, 13, 5, 0, 6291163412844499803, 7], dtype=tf.float64, minval=-1024, maxval=1024)
```

This is similar to writing the overflow itself, it won't show up in a real model. Hi @shijy16 ,

This has been fixed. tested with TF2.16v and found raising an exception rather than check fail. Refer [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/30733cd9b19b8509065f590c1c747e45/60277_nightly_success.ipynb). 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.TridiagonalMatMul` due to check-fail(multiply overflow) in the latest version of TensorFlow.
```


### Standalone code to reproduce the issue

```shell
import tensorflow as tf
with tf.device("CPU"):
    superdiag = tf.random.uniform([5, 13, 5, 0, 6291163412844499803, 7], dtype=tf.float64, minval=-1024, maxval=1024)
    maindiag = tf.random.uniform([], dtype=tf.float64, minval=-1024, maxval=1024)
    subdiag = tf.random.uniform([9, 3, 3], dtype=tf.float64, minval=-1024, maxval=1024)
    rhs = tf.random.uniform([0, 3, 2, 8, 12, 8], dtype=tf.float64, minval=-1024, maxval=1024)
    res = tf.raw_ops.TridiagonalMatMul(
        superdiag=superdiag,
        maindiag=maindiag,
        subdiag=subdiag,
        rhs=rhs,
    )
```


### Relevant log output

```shell
2023-04-09 14:03:29.795879: 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 14:03:29.841868: 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 14:03:30.668006: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-04-09 14:03:32.219934: 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 14:03:32.257496: F tensorflow/core/framework/tensor_shape.cc:201] Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Encountered overflow when multiplying 6291163412844499803 with 7, result: -1
Aborted (core dumped)
```
</details>