Tensorflow 2.x: call model inference using C/C++ API from inputs, allocated in GPU memory
I am also interested in running a TF model with inputs and outputs allocated on the GPU in the C API. This feature would be very useful. Unfortunately, this doesn't seem to be very easy if there has been no response for a month
### Issue type

Support

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

No

### Source

binary

### TensorFlow version

v2.13.0-rc2-7-g1cb1a030a62

### Custom code

Yes

### OS platform and distribution

Linux Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

A100 80Gb

### Current behavior?

I’d like to use TensorFlow 2.13 in scientific simulation code, written in C++, via [C-API](https://www.tensorflow.org/install/lang_c). The code runs simulation on GPU, so all necessary input data for the model are already placed on GPU too.

I need to prepare input for the model, that contains multiple TF_Tensors.

My question is: Is it possible to control, where to place TF_Tensor? Can I make it point to existing on-GPU array to avoid CPU-to-GPU memory transfer?

If wrapping TF tensor around existing data is not possible, would that be possible to copy memory within GPU?

I found [TF_AllocatorAttributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/c/tf_tensor.h#L33) struct, that contains placement flag and it is used, for example, in [TF_AllocateTemp](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/c/kernels.h#L503), but this function requires also TF_OpKernelContext ctx. Unfortunately it is not clear for me where to take it and is it safe at all to use it in C-API

### Standalone code to reproduce the issue

```shell
-
```


### Relevant log output

_No response_