onnxruntime

[TensorRT EP] Load precompiled TRT engine file directly  (#18217)

When the TRT engine cache (precompiled engine) is present, it doesn't
make sense to go over the processes of model verification, model
optimization, TRT EP's GetCapability(), TRT EP's model proto
reconstruction, calling TRT parser and engine compilation.
This PR makes TRT EP skip those processes and directly load the engine
to perform inference.

The feature request:
https://github.com/microsoft/onnxruntime/issues/18072

Features:

- Replace original model with TRT engine wrapped ONNX model. It can save
a lot of time as mentioned above.

- How to get TRT engine wrapped ONNX model?
1. Set `trt_dump_ep_context_model` provider option to "true" and run the
inference. You will find the "xxx_wrapper.onnx" at the engine cache
path. (The same logic of generating engine cache)
    2. Use gen_trt_engine_wrapper_onnx_model.py

- Three provider options are added, 
`trt_dump_ep_context_model`: Enable dump wrapped onnx model by TRT EP
`trt_ep_context_embed_mode`: Add embed_mode as attribute. 0 means engine
cache path, 1 means engine binary data.
`trt_ep_context_compute_capability_enable`: Add hardware_arch as
attribute. When running the model, TRT EP will check consistency between
model's hardware_arch and GPU's compute capability.

- When the engine cache path is given in the wrapped model, TRT EP will
first search for the engine file using the path (relative to model
path), if it can't find it, it will change to use the path as it is
(depends on user, could be relative to working dir or absolute path)

Note: 

1. This PR includes the change of
https://github.com/microsoft/onnxruntime/pull/17751


Constraints:

1. The whole model should be fully supported by TRT. 
4. Users need to make sure the engine is built with min/max/opt
optimization profiles that large enough to cover the range of all
inputs. TRT EP will simply fail and won't rebuild the engine if the
input shape is out of range during runtime.