Benchmarks: Add Mixture of Experts Model

[dpower4]

Added MoE model using MixtralConfig.

1. Added 8x7b and 8x22b variants
2. Requires high VRAM as all experts are loaded in memory. Thus, disabled training due to memory constraint on test worker.

[codecov]

Codecov Report

Attention: Patch coverage is 88.23529% with 16 lines in your changes missing coverage. Please review.

Project coverage is 86.47%. Comparing base (deef9a3) to head (434e442).
Report is 1 commits behind head on main.

Files with missing lines	Patch %	Lines
...enchmarks/model_benchmarks/pytorch_mixtral_impl.py	86.20%	16 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #679      +/-   ##
==========================================
+ Coverage   86.44%   86.47%   +0.03%     
==========================================
  Files         100      102       +2     
  Lines        7406     7541     +135     
==========================================
+ Hits         6402     6521     +119     
- Misses       1004     1020      +16     

Flag	Coverage Δ
cpu-python3.10-unit-test	71.59% <38.80%> (-0.61%)	⬇️
cpu-python3.12-unit-test	71.59% <38.80%> (-0.61%)	⬇️
cpu-python3.7-unit-test	70.65% <9.55%> (-1.15%)	⬇️
cuda-unit-test	83.98% <85.82%> (+0.03%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.
• 📦 JS Bundle Analysis: Save yourself from yourself by tracking and limiting bundle sizes in JS merges.

[Copilot]

Pull Request Overview

This PR introduces a new Mixture of Experts (MoE) model variant using MixtralConfig with two parameter sets (8x7b and 8x22b), along with associated tests and documentation updates. It includes adding version checks for Python, conditional imports, benchmark registrations, and exporting support for the new model.

Reviewed Changes

Copilot reviewed 6 out of 7 changed files in this pull request and generated 1 comment.

Show a summary per file

File	Description
tests/helper/decorator.py	Added a Python version check decorator for tests.
tests/benchmarks/model_benchmarks/test_pytorch_mixtral.py	Introduced tests for the new Mixtral MoE benchmark (8x7b variant).
superbench/benchmarks/model_benchmarks/pytorch_mixtral.py	Implemented the Mixtral benchmark model and registered two variants.
superbench/benchmarks/model_benchmarks/init.py	Updated module imports and all to conditionally include MoE model.
superbench/benchmarks/micro_benchmarks/_export_torch_to_onnx.py	Extended ONNX export support to include Mixtral models.
docs/user-tutorial/benchmarks/model-benchmarks.md	Added documentation for MoE models.

Files not reviewed (1)

• docs/superbench-config.mdx: Language not supported

Comments suppressed due to low confidence (1)

superbench/benchmarks/micro_benchmarks/_export_torch_to_onnx.py:28

• [nitpick] Consider renaming this class to Torch2ONNXExporter to adhere to standard Python CamelCase naming conventions.

class torch2onnxExporter():