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Co-authored-by: Chris Austen <causten@users.noreply.github.com>

Ref Unique Operator only

Bumps [rocm-docs-core](https://github.com/RadeonOpenCompute/rocm-docs-core) from 0.28.0 to 0.29.0.
- [Release notes](https://github.com/RadeonOpenCompute/rocm-docs-core/releases)
- [Changelog](https://github.com/RadeonOpenCompute/rocm-docs-core/blob/develop/CHANGELOG.md)
- [Commits](https://github.com/RadeonOpenCompute/rocm-docs-core/compare/v0.28.0...v0.29.0

)

---
updated-dependencies:
- dependency-name: rocm-docs-core
  dependency-type: direct:production
  update-type: version-update:semver-minor
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Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

Bumps [cryptography](https://github.com/pyca/cryptography) from 41.0.4 to 41.0.6.
- [Changelog](https://github.com/pyca/cryptography/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pyca/cryptography/compare/41.0.4...41.0.6

)

---
updated-dependencies:
- dependency-name: cryptography
  dependency-type: indirect
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Add min and max reduction modes
Implement duplicate index handling
Add corresponding reference, onnx, and verify tests.

This example show how to use HuggingFace's Stable Diffusion v2.1
(https://huggingface.co/stabilityai/stable-diffusion-2-1) model
with migraphx's python frontend.

Simplify what we parse the ONNX operator ConstantOfShape into a literal for a static shape

Introduce dilations attribute to pooling operators reference implementation.

This PR introduces the support of Windows resource files to Embed.cmake. It is ON by default on Windows, and when it is OFF *.cpp files will be used. The same applies to Linux - ON -> *.o (LD) or OFF -> *.cpp .

This PR fixes building resources on Linux with ld and objcopy commands.

Converts dynamic reshapes with constant output dimensions to a static reshape instruction
Intended to be used after split_single_dyn_dim pass and after find_static_dimensions_of makes shape calculations doable at compile-time.

Handles all 4 Fp8 dtypes listed here : https://onnx.ai/onnx/technical/float8.html
Follows saturation/clipping logic from table there as well : https://onnx.ai/onnx/technical/float8.html#cast
Only adding fp8e4m3fnuz in MIGraphX IR for now.

Reworked the simplify_qdq pass to support:

Per-axis quantization (ie. allow 1D scales and zero points)
Allow broadcast and transpose ops between dq and quant_op