can we customize memory allocation functions(like malloc/free) for inference with C api? 
@dandlake,
Could you please elaborate about your Feature. Also, please specify the Use Cases for this feature. Thank you! In our use case, we have multiple processes to handle various workloads. They all need to do ML inference. We are tight on memory, so we need to load the model once in shared memory, instead of every process loading a copy of the model themselves. This way, all the processes can use the model to do inference. Can this be supported?
<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Feature Request

### Have you reproduced the bug with TF nightly?

No

### Source

source

### Tensorflow Version

2.8

### Custom Code

No

### 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 Behaviour?

When using C api to do inference, I want to customize the memory allocation functions like malloc/free to control how tensorflow allocates memory. I want to make tensorflow use a separate area of memory in some shared memory region to load the model. Is there a way to do this?

### Standalone code to reproduce the issue

```shell
N/A
```


### Relevant log output

_No response_</details>