OOM when create a TensorShape {0, 16,16}
@xuesu Could you consider using dynamic shapes (e.g., tf.TensorShape(None, 16, 16)) if the zero-sized dimension is expected during model execution?
Thank you! I couldn't control the real shape here, but of course, I can add a 0-check. However,  I think there should be an assertion or a warning instead of just OOM.
### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

2.14.1

### Custom code

Yes

### OS platform and distribution

Linux Ubuntu22.04

### Mobile device

_No response_

### Python version

3.10

### Bazel version

_No response_

### GCC/compiler version

clang-16

### CUDA/cuDNN version

11.8;8.7.0

### GPU model and memory

_No response_

### Current behavior?

If I create a normal shape like {2,5,8} then nothing weird will happen.
If I create a shape like {0, 16,16} then no warning will be output, the program will stuck for several seconds, then OOM or Segmentation fault (core dumped).

### Standalone code to reproduce the issue

```shell
#include "tensorflow/cc/client/client_session.h"
#include "tensorflow/cc/framework/grad_op_registry.h"
#include "tensorflow/cc/framework/gradient_checker.h"
#include "tensorflow/cc/framework/gradients.h"
#include "tensorflow/cc/framework/testutil.h"
#include "tensorflow/cc/ops/math_ops.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/lib/random/random.h"
#include "tensorflow/tsl/platform/status.h"
using namespace tensorflow;
using namespace tensorflow::ops;
int main(){
  TensorShape shape({0, 16, 16});
 // printf("shape: %d, %d, %d\n", shape.dim_sizes()[0], shape.dim_sizes()[1], shape.dim_sizes()[2]);
}
```
```


### Relevant log output

```shell
with libfuzzer:

INFO: Seed: 1375624859                                                                                                                                                                      [1590/1851]
INFO: Loaded 3 modules   (2749475 inline 8-bit counters): 139385 [0x7f275352b8a0, 0x7f275354d919), 2607128 [0x7f276dda0300, 0x7f276e01cb18), 2962 [0x55a5f8763978, 0x55a5f876450a), 
INFO: Loaded 3 PC tables (2749475 PCs): 139385 [0x7f275354d920,0x7f275376e0b0), 2607128 [0x7f276e01cb18,0x7f27707e4c98), 2962 [0x55a5f8764510,0x55a5f876fe30), 
INFO: -max_len is not provided; libFuzzer will not generate inputs larger than 4096 bytes
INFO: A corpus is not provided, starting from an empty corpus
#2      INITED cov: 2 ft: 2 corp: 1/1b exec/s: 0 rss: 301Mb
shape: 0, 16, 16
2023-12-30 09:27:54.078978: 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: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
==751782== ERROR: libFuzzer: out-of-memory (used: 2049Mb; limit: 2048Mb)
   To change the out-of-memory limit use -rss_limit_mb=<N>

MS: 4 InsertRepeatedBytes-CrossOver-InsertByte-InsertRepeatedBytes-; base unit: adc83b19e793491b1c6ea0fd8b46cd9f32e592fc
0xa,0xff,0xff,0xff,0x2c,0xff,0xff,0xff,0xa,0xff,0xff,0xff,0xff,
\012\377\377\377,\377\377\377\012\377\377\377\377 
artifact_prefix='./'; Test unit written to ./oom-ff3224e0c400438358258acad47e95d83a32a8bc
Base64: Cv///yz///8K/////w==
SUMMARY: libFuzzer: out-of-memory
```
standalone:
```
Segmentation fault (core dumped)
```
```
