### Description
This extends the existing pad_fusion for AveragePool operator i.e. fuse
Pad if it is followed by AveragePool operator.



### Motivation and Context
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- If it fixes an open issue, please link to the issue here. -->

* Fix migraphx build error caused by
#21598:
Add a conditional compile on code block that depends on ROCm >= 6.2.
Note that the pipeline uses ROCm 6.0.

Unblock orttraining-linux-gpu-ci-pipeline and
orttraining-ortmodule-distributed and orttraining-amd-gpu-ci-pipeline
pipelines:
* Disable a model test in linux GPU training ci pipelines caused by
#19470:
Sometime, cudnn frontend throws exception that cudnn graph does not
support a Conv node of keras_lotus_resnet3D model on V100 GPU.
Note that same test does not throw exception in other GPU pipelines. The
failure might be related to cudnn 8.9 and V100 GPU used in the pipeline
(Amper GPUs and cuDNN 9.x do not have the issue).
The actual fix requires fallback logic, which will take time to
implement, so we temporarily disable the test in training pipelines.
* Force install torch for cuda 11.8. (The docker has torch 2.4.0 for
cuda 12.1 to build torch extension, which it is not compatible cuda
11.8). Note that this is temporary walkround. More elegant fix is to
make sure right torch version in docker build step, that might need
update install_python_deps.sh and corresponding requirements.txt.
* Skip test_gradient_correctness_conv1d since it causes segment fault.
Root cause need more investigation (maybe due to cudnn frontend as
well).
* Skip test_aten_attention since it causes assert failure. Root cause
need more investigation (maybe due to torch version).
* Skip orttraining_ortmodule_distributed_tests.py since it has error
that compiler for torch extension does not support c++17. One possible
fix it to set the following compile argument inside setup.py of
extension fused_adam: extra_compile_args['cxx'] = ['-std=c++17'].
However, due to the urgency of unblocking the pipelines, just disable
the test for now.
* skip test_softmax_bf16_large. For some reason,
torch.cuda.is_bf16_supported() returns True in V100 with torch 2.3.1, so
the test was run in CI, but V100 does not support bf16 natively.
* Fix typo of deterministic

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…soft/onnxruntime into user/sumita/cherrypick-pad