Unexpect 0 in the API combination on GPU and CPU when invoking tf.GradientTape() and tf.math.reduce_prod(x)
Hi **@beanduan22** ,
Sorry for the delay. The tf.math.reduce_prod function multiplies the elements along the specified axis, and if there are zeros in the tensor, the result will be zero.If you want to compute the gradient of the product of elements in x, you might want to handle the zeros differently, for example, by adding a small constant to avoid issues with zero gradients. I added that small constant in the code and provided a [gist](https://colab.research.google.com/gist/Venkat6871/29cd011588bce1fc07f1dde6b1d0efba/62555_2-14_2-15_tf-nightly.ipynb) for your reference. Could you go through it once and let me know the update.

Thank you! 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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### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

tf 2.13

### 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?

```python
import tensorflow as tf
import numpy as np

with tf.device("/cpu:0"):
  def test(x):
    with tf.GradientTape() as tape_1:
      tape_1.watch(x)
      w = tf.math.reduce_prod(x)
    grad_1 = tape_1.gradient(w, x)
    return grad_1

  x = tf.constant([[0,0.1,0.2],[0,1,2.]],dtype=tf.float32)
  cpu_output_1 = test(x)

with tf.device("/gpu:0"):
  def test(x):
    with tf.GradientTape() as tape_1:
      tape_1.watch(x)
      w = tf.math.reduce_prod(x)
    grad_1 = tape_1.gradient(w, x)
    return grad_1

  x = tf.constant([[0, 0.1,0.2],[0, 1, 2.]],dtype=tf.float32)
  gpu_output_1 = test(x)

print(cpu_output_1)
print(gpu_output_1)

### Standalone code to reproduce the issue

```shell
Unexpect 0 in the API combination on GPU and CPU when invoking tf.GradientTape() and tf.math.reduce_prod(x)
```


### Relevant log output

```shell
tf.Tensor(
[[0. 0. 0.]
 [0. 0. 0.]], shape=(2, 3), dtype=float32)
tf.Tensor(
[[0. 0. 0.]
 [0. 0. 0.]], shape=(2, 3), dtype=float32)
```
