inconsistency when invoking tf.math.top_k and tf.negative
The tf.math.top_k operation returns both the values and their indices, making it incompatible with the unary - operator. You'll need to specify whether you want to apply the negative operation to the values or the indices.

Solution 1 : 
values, indices = tf.math.top_k(A)
neg_values = -values

Solution 2: create a wrapper or a helper function
def custom_negate_top_values(tensor):
    top_values, top_indices = tf.math.top_k(tensor)
    neg_top_values = -top_values
    return neg_top_values, top_indices


Other Solution : can be to make changes to source code so it automatically support " - " unary operator. can anyone help me find the source code for math.top_k .
 Hi @beanduan22 ,
Sorry for the delay. I Was able to reproduce the issue on colab using TF v2.14 , 2.15, and TF-nightly, Please find the [gist](https://colab.research.google.com/gist/Venkat6871/3bc44fc05599118792714bcd635ceaa1/62563_2-14_2-15-nightly.ipynb) here for reference. We need to dig for this issue. I will give an update in a few days.

Thank you! Hi **@beanduan22**,

When you apply the negative operation directly to the result of tf.math.top_k, you are trying to negate the entire TopKV2 object, which leads to a TypeError.
You need to separately apply the negative operation to the values part of the TopKV2 result and keep the indices unchanged. Here I am adding a [gist ](https://colab.research.google.com/gist/Venkat6871/852e781277ede158d660b722fbb447f9/62563_2-15-v.ipynb)for your reference please go through it once.

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

A = tf.constant([0, -2, 1, -4, 3])
print(-A)
print(tf.negative(A))
y = tf.negative(tf.math.top_k(tf.negative(A)))
x = -tf.math.top_k(-A)
print(y)
print(x)

#######################################
import tensorflow as tf

A = tf.constant([0, -2, 1, -4, 3])
print(-A)
print(tf.negative(A))
print(tf.math.top_k(tf.negative(A)))
y = tf.negative(tf.math.top_k(tf.negative(A)))
print(y)

### Standalone code to reproduce the issue

```shell
when invoking tf.math.top_k, the tf.negative have different result with -
```


### Relevant log output

```shell
tf.Tensor([ 0  2 -1  4 -3], shape=(5,), dtype=int32)
tf.Tensor([ 0  2 -1  4 -3], shape=(5,), dtype=int32)
Traceback (most recent call last):
  File "C:\Users\uqbduan\Desktop\pythonProject\test.py", line 9, in <module>
    x = -tf.math.top_k(-A)
TypeError: bad operand type for unary -: 'TopKV2'
##################################
tf.Tensor([ 0  2 -1  4 -3], shape=(5,), dtype=int32)
tf.Tensor([ 0  2 -1  4 -3], shape=(5,), dtype=int32)
TopKV2(values=<tf.Tensor: shape=(1,), dtype=int32, numpy=array([4])>, indices=<tf.Tensor: shape=(1,), dtype=int32, numpy=array([3])>)
tf.Tensor(
[[-4]
 [-3]], shape=(2, 1), dtype=int32)
```
