Support for asynchronous execution in TensorFlow DLPack interface
I believe you are fundamentally misunderstanding the original issue in #61420.  They are not looking for an example network, and I doubt using python async mechanisms will address the issue.  It has to do with the internal workings of `tensorflow.experimental.dlpack.to_dlpack` and `tensorflow.experimental.dlpack.from_dlpack` functions, which are currently synchronous operations in the TensorFlow execution graph, but the user would like an asynchronous interface.
This pull request addresses the issue #61504 and adds the following functions:

Asynchronous processing: The code uses multiprocessing to asynchronously process the input tensors. This allows the code to process multiple tensors in parallel, which can improve the performance of the code.

Error handling: The code handles errors that may occur during the processing of the input tensors. This includes errors such as invalid input tensors, network failures, and out of memory errors.

Integration with other components: The code can be integrated with other components, such as data loading libraries and model inference libraries. This allows the code to be used in a wider variety of applications.

Variable number of networks and CPUs: The code can be used to process a variable number of networks and CPUs. This allows the code to be scaled up or down to meet the needs of the application.