Refactor CoreMLExecution to C++ bridge class

[1enn0]

Description

Refactor Objective-C++ class CoreMLExecution into existing C++ bridge class onnxruntime::coreml::Execution.

Motivation and Context

Fixes #21708.

[1enn0]

@microsoft-github-policy-service agree company="Steinberg Media Technologies GmbH"

[1enn0]

@edgchen1 thanks for your thorough review. I have to admit the previous state was a bit half-baked on my part. Hopefully I did not miss anything this time.

I also ran clang-format on this file which makes it a bit harder to review my changes now (looks like this has not been done for a while).

[edgchen1]

I also ran clang-format on this file which makes it a bit harder to review my changes now (looks like this has not been done for a while).

Thanks for considering the formatting. Actually, we do have a CI check that ensures that (most of) our code is formatted, including this file. This repo has its own .clang-format files, like this one.

In particular, we have a convention of a 120 character line length limit and the formatting changes in this PR seem to assume a lower limit. If possible, would you mind reverting the formatting changes and re-running clang-format with the .clang-format files in this repo? It should be the default behavior of clang-format if no formatting configuration options are given.

[1enn0]

If possible, would you mind reverting the formatting changes and re-running clang-format with the .clang-format files in this repo?

Should be fine now...

[edgchen1]

/azp run iOS CI Pipeline

[azure-pipelines[bot]]

Azure Pipelines successfully started running 1 pipeline(s).

[edgchen1]

/azp run Windows ARM64 QNN CI Pipeline,Windows x64 QNN CI Pipeline,Windows CPU CI Pipeline,Windows GPU CUDA CI Pipeline,Windows GPU DML CI Pipeline,Windows GPU Doc Gen CI Pipeline,Windows GPU TensorRT CI Pipeline,ONNX Runtime Web CI Pipeline,Linux CPU CI Pipeline,Linux CPU Minimal Build E2E CI Pipeline

[edgchen1]

/azp run Linux GPU CI Pipeline,Linux GPU TensorRT CI Pipeline,Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline,orttraining-amd-gpu-ci-pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline,orttraining-ortmodule-distributed,onnxruntime-binary-size-checks-ci-pipeline

[edgchen1]

/azp run Big Models,Linux Android Emulator QNN CI Pipeline,Android CI Pipeline,iOS CI Pipeline,ONNX Runtime React Native CI Pipeline

[azure-pipelines[bot]]

Azure Pipelines successfully started running 5 pipeline(s).

[azure-pipelines[bot]]

Azure Pipelines successfully started running 9 pipeline(s).

[azure-pipelines[bot]]

Azure Pipelines successfully started running 10 pipeline(s).

[edgchen1]

/azp run Windows ARM64 QNN CI Pipeline,Windows x64 QNN CI Pipeline,Windows CPU CI Pipeline,Windows GPU CUDA CI Pipeline,Windows GPU DML CI Pipeline,Windows GPU Doc Gen CI Pipeline,Windows GPU TensorRT CI Pipeline,ONNX Runtime Web CI Pipeline,Linux CPU CI Pipeline,Linux CPU Minimal Build E2E CI Pipeline

[edgchen1]

/azp run Linux GPU CI Pipeline,Linux GPU TensorRT CI Pipeline,Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline,orttraining-amd-gpu-ci-pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline,orttraining-ortmodule-distributed,onnxruntime-binary-size-checks-ci-pipeline

[edgchen1]

/azp run Big Models,Linux Android Emulator QNN CI Pipeline,Android CI Pipeline,iOS CI Pipeline,ONNX Runtime React Native CI Pipeline

[azure-pipelines[bot]]

Azure Pipelines successfully started running 5 pipeline(s).

[azure-pipelines[bot]]

Azure Pipelines successfully started running 9 pipeline(s).

[azure-pipelines[bot]]

Azure Pipelines successfully started running 10 pipeline(s).