Numpy Based Indexing and Updates in Tensors
or at least support this type of indexing after ``experimental_enable_numpy_behavior`` has been applied. Are there any updates or plan for this feature? @Impure-King,
Have you got the chance to test the code with `tf.tensor_scatter_nd_update`, where I can see that replacing the specific index value of a tensor using the mentioned API.

```
array = tf.constant([1, 2, 3, 4])
value = 1
indices = [1]

new_array = tf.tensor_scatter_nd_update(array, [indices], [value])
print(new_array)
```

Thank you! I have, but the reason I added this as a ``feature request`` is because a function is a bit verbose to replace standard indexing. I was wondering if it was possible to add standard index update instead, since it would make ease use better. Hi as of now, you can do the update using the following method.

```python
from tensorflow.compiler.tf2xla.python import xla

output = xla.dynamic_update_slice(input, update, indices)
```

There has been an RFC proposed, but there is not much progress on it so far.

You can close this issue and track the progress in the on going discussion here 
https://github.com/tensorflow/tensorflow/issues/33131

And here is the RFC for more details. https://github.com/tensorflow/community/pull/433 I see.
### Issue type

Feature Request

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

Yes

### Source

binary

### TensorFlow version

2.15

### Custom code

Yes

### OS platform and distribution

WSL Ubuntu 22.04.3 LTS

### Mobile device

N/A

### Python version

3.11

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

N/A

### GPU model and memory

_No response_

### Current behavior?

Can eager tensors finally get support for numpy indexing? Since eager mode is design to reduce programming fatigue, it personally makes no sense to index using a function like ``tf.scatter_nd``. It also makes the code less readable, when implementing SOTA models and considering the expectations of modern frameworks, it might be better to overhaul the immutability.

### Standalone code to reproduce the issue

```shell
This code should ideally work:


import tensorflow as tf
from tensorflow import keras

array = tf.constant([1, 2, 3, 4])
array[1] = 1

print(array) # [1, 1, 3, 4]
```


### Relevant log output

_No response_