### What does this PR do?
Type of change: Documentation update
- Update documentation guide for ONNX INT4 PTQ on Windows cuda13 host -
mention about compatible onnxruntim-gpu and cupy-cuda13x packages.
### Testing
- Windows's onnx_ptq\genai_llm INT4 PTQ example with a 1B genai-cuda-ep
ONNX model + local doc building
### Before your PR is "*Ready for review*"
Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)
and your commits are signed (`git commit -s -S`).
Make sure you read and follow the [Security Best
Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors)
(e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(...,
weights_only=False)`, `pickle`, etc.).
- Is this change backward compatible?: ✅ / ❌ / N/A <!--- If ❌, explain
why. -->
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: ✅ / ❌ / N/A
<!--- Mandatory -->
- Did you write any new necessary tests?: ✅ / ❌ / N/A <!--- Mandatory
for new features or examples. -->
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
✅ / ❌ / N/A <!--- Only for new features, API changes, critical bug fixes
or backward incompatible changes. -->
- Did you get Claude approval on this PR?: ✅ / ❌ / N/A <!--- Run
`/claude review`. NVIDIA org members can self-trigger for complex
changes; orthogonal to CodeRabbit. -->
### Additional Information
<!-- E.g. related issue. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Documentation**
* Clarified Windows CUDA prerequisites for calibration and
GPU-accelerated quantization.
* Added setup guidance for CUDA 12 and CUDA 13.x, including compatible
packages and cuDNN requirements.
* Expanded installation verification steps for CUDA, ONNX Runtime, and
CuPy.
* Updated the GenAI LLM example with CUDA version compatibility
guidance.
* **Enhancements**
* Added runtime logging of detected CUDA environment paths and version
details during quantization.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Signed-off-by: vipandya <vipandya@nvidia.com>
Ubuntu 26.04 is here and very soon, NVIDIA PyTorch containers will ship
with Python 3.14 requiring us to enable untested support to unblock them
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* DFlash offline speculative decoding training
* MXFP4→NVFP4 weight conversion support
* Shared hidden-state dump utilities
* Updated DeepSeek PTQ calibration defaults
* **Chores**
* Added Python 3.14 support; updated Python requirement to <3.15
* **Documentation**
* Updated installation documentation for Python version compatibility
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
### What does this PR do?
Type of change: ? documentation
<!-- Details about the change. -->
Update windows documentation for onnxruntime quantization with Cuda13.x
### Before your PR is "*Ready for review*"
Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)
and your commits are signed (`git commit -s -S`).
Make sure you read and follow the [Security Best
Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors)
(e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(...,
weights_only=False)`, `pickle`, etc.).
- Is this change backward compatible?: N/A <!--- If ❌, explain why. -->
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: N/A <!---
Mandatory -->
- Did you write any new necessary tests?: N/A <!--- Mandatory for new
features or examples. -->
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A <!--- Only for new features, API changes, critical bug fixes or
backward incompatible changes. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Documentation**
* Updated Windows installation guide with CUDA 13.x-specific setup
instructions for GPU-accelerated dependencies, including CuPy and ONNX
Runtime configuration with nightly builds.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Signed-off-by: ynankani <ynankani@nvidia.com>
## Summary
- Replaces the old pip license notice ("Please review the license terms
of ModelOpt and any dependencies before use") with the Legal-approved
wording: "Model Optimizer will download and install additional
third-party open source software projects. Review the license terms of
these open source projects before use."
- Adds a generic container license review notice ("Before pulling and
using the container images, please review their respective license
terms.") to the Linux installation doc (Docker tab) and README.
- Adds a `.. note::` with the pip notice to the Windows installation
page (covers both standalone and Olive child pages).
- Expands the README container section to explicitly list all four
recommended NVIDIA container images (`pytorch`, `nemo`, `tensorrt-llm`,
`tensorrt`).
## Test plan
- [x] Verify rendered docs look correct (`nox -s docs`)
- [x] Confirm legal notices appear in Linux, Windows, and README install
sections
🤖 Generated with [Claude Code](https://claude.com/claude-code)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Documentation**
* Updated installation guides with explicit references to supported
NVIDIA container images (PyTorch, NeMo, TensorRT-LLM and variants),
clarified pre-installed Model Optimizer in some images, and added notes
to review each container’s license terms; clarified conditional
environment setup wording and local install license guidance.
* **Chores**
* Updated project license header year.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
### What does this PR do?
- Add experimental support for transformers >=5.0 and remove deprecated
usages:
https://github.com/huggingface/transformers/blob/main/MIGRATION_GUIDE_V5.md
- ⚠️ For accelerate examples that used `--warmup-ratio: float`
(deprecated in 5.x), we now change it to `--warmup-steps: float | int`
which works as ratio if float but only for 5.x. For 4.x, it will error
out if float and prompt user to change back to `--warmup-ratio` or pass
an int absolute step count.
- ⚠️ Unified Hugging Face checkpoint export for quantized checkpoints
may not work for some models with transformers>=5.0 yet as it requires a
lot of fixes (e.g. change in how MoE experts are organized)
- ~Add Workaround for TRT-LLM's import of deprecated transformers
functions so trt-llm based gpu unit tests work fine. Still deployment
for models needs proper fixes directly in TRT-LLM hence llm/vlm ptq
example tests still run with transformers 4.57~
- Everything except PTQ and Export (mainly MoE) should work fine with
transformers>=5.0
- Bump min torch to 2.8 and enable 2.11 cicd testing
- NOTE: Upcoming Nemo:26.04 container comes with transformers 5.3
### Testing
<!-- Mention how have you tested your change if applicable. -->
- [x] CI/CD tests passing
- [x] Manually tested unit tests, gpu tests with transformers 4.56 and
5.4
- [x] Manually tested example tests (except trt-llm container tests)
with transformers 4.56 and 5.4
- [x] 2-gpu nightly CICD tests manually triggered and passing: [gpu
tests](https://github.com/NVIDIA/Model-Optimizer/actions/runs/23867257540),
[example
tests](https://github.com/NVIDIA/Model-Optimizer/actions/runs/23867260643)
### Before your PR is "*Ready for review*"
Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)
and your commits are signed (`git commit -s -S`).
Make sure you read and follow the [Security Best
Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors)
(e.g. avoiding hardcoded `trust_remote_code=True`, using
`torch.load(..., weights_only=True)`, avoiding `pickle`, etc.).
- Is this change backward compatible?: ✅ <!--- If ❌, explain why. -->
- If you copied code from any other source, did you follow IP policy in
[CONTRIBUTING.md](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md#-copying-code-from-other-sources)?:
N/A <!--- Mandatory -->
- Did you write any new necessary tests?: ✅ <!--- Mandatory for new
features or examples. -->
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
✅ <!--- Only for new features, API changes, critical bug fixes or
backward incompatible changes. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Make remote-code usage opt-in via a configurable --trust_remote_code
flag across examples and tools.
* **Bug Fixes**
* Improve checkpoint/resume detection and related training guidance to
avoid erroneous errors.
* **Refactor**
* Consolidate dtype/config naming, switch warmup settings from ratio →
steps, and unify tokenizer invocation patterns.
* **Documentation**
* Simplify changelog title and add misc notes for release 0.44.
* **Chores**
* Remove scheduled PR-branch cleanup workflow and relax/remove several
transformers version pins.
* **Tests**
* Adjust test gates, skips, and structures to align with updated deps
and behaviors.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
### What does this PR do?
Type of change: Minor documentation update
- Update documentation (olive installation instructions) to mention
install commands for CUDA EP packages for ORT / ORT-genai.
### Testing
- Locally checked the readme, doc.
### Before your PR is "*Ready for review*"
Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)
and your commits are signed (`git commit -s -S`).
Make sure you read and follow the [Security Best
Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors)
(e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(...,
weights_only=False)`, `pickle`, etc.).
- Is this change backward compatible?: ✅ / ❌ / N/A <!--- If ❌, explain
why. -->
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: ✅ / ❌ / N/A
<!--- Mandatory -->
- Did you write any new necessary tests?: ✅ / ❌ / N/A <!--- Mandatory
for new features or examples. -->
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
✅ / ❌ / N/A <!--- Only for new features, API changes, critical bug fixes
or backward incompatible changes. -->
### Additional Information
<!-- E.g. related issue. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Documentation**
* Updated Windows Olive installation guidance to recommend CUDA-based
ONNX Runtime packages instead of DirectML and added a link to ONNX
Runtime’s Execution-Provider docs for alternative EPs and requirements.
* Simplified Windows examples by removing explicit package install
commands and pointing users to the consolidated Olive setup
instructions.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: vipandya <vipandya@nvidia.com>
### What does this PR do?
- Remove `examples/nemo_run` and other deprecated Nemo 2.0 references
- Add Megatron-Bridge example links where missing
<!-- Details about the change. -->
### Testing
<!-- Mention how have you tested your change if applicable. -->
### Before your PR is "*Ready for review*"
Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)
and your commits are signed (`git commit -s -S`).
Make sure you read and follow the [Security Best
Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors)
(e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(...,
weights_only=False)`, `pickle`, etc.).
- Is this change backward compatible?: ✅ <!--- If ❌, explain why. -->
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: N/A <!---
Mandatory -->
- Did you write any new necessary tests?: N/A <!--- Mandatory for new
features or examples. -->
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
✅ <!--- Only for new features, API changes, critical bug fixes or
backward incompatible changes. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Deprecations**
* Removed deprecated NeMo 2.0 support and related example flows and
utilities.
* **Documentation**
* Updated docs and examples to emphasize Megatron-Bridge / Megatron-LM
and refreshed technique/deployment guidance and links.
* **New Features**
* Added CLI options for additional parallelism (context/expert
tensor/expert model) in Megatron-Bridge distillation.
* **Chores**
* Removed legacy CI configs and refreshed container image tags across
examples and docs.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Fixes https://github.com/NVIDIA/Model-Optimizer/issues/217
## Summary
- Bump `requires-python` from `>=3.10,<3.13` to `>=3.10,<3.14` to
formally include Python 3.13
- Add explicit Python 3.10–3.13 PyPI classifiers for better
discoverability
- Add `py313` to tox CPU unit test and partial-install environment
matrices
- Add Python 3.10–3.13 to the `multi-py` CI matrix in `unit_tests.yml`
## Background
Python 3.13 was previously excluded by the `<3.13` upper bound. Testing
in a related repo with `--ignore-requires-python` confirmed that the
library installs and runs correctly under Python 3.13. This PR lifts the
restriction and wires up CI to verify it going forward.
## Test plan
- [ ] CI `multi-py` job passes on `py313-torch210-tf_latest-unit`
- [ ] `tox -e py313-torch210-tf_latest-unit` passes locally (requires
Python 3.13 installed)
- [ ] `tox -e py313-partial-unit-torch` passes locally
- [ ] No regressions on existing Python 3.10/3.11/3.12 matrix jobs
🤖 Generated with [Claude Code](https://claude.com/claude-code)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Chores**
* Extended Python support: minimum remains 3.10; added official support
up through 3.13 (upper bound advanced accordingly).
* **Tests**
* CI and test matrix expanded to include experimental Python 3.13
coverage.
* **Documentation**
* Installation docs and changelog updated to reflect Python 3.13
support.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Ivan Basov <ibasov@nvidia.com>
Signed-off-by: Ivan Basov <5455484+ivanbasov@users.noreply.github.com>
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
**Type of change:** Bug fix
**Overview:** Upgrade ORT to 1.24.x to fix various bugs (5615343,
5597780, 5371126).
TODO: Verify no regressions by bumping ORT once @ajrasane is back
## Usage
See each bug.
## Testing
See each bug.
## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->
- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes <!--- If No, explain why.
-->
- **Did you write any new necessary tests?**: No
- **Did you add or update any necessary documentation?**: No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Bug Fixes
* Upgraded ONNX Runtime to version 1.24.2, addressing multiple reported
issues
* Updated system requirements and installation documentation to reflect
the new ONNX Runtime version
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: gcunhase <4861122+gcunhase@users.noreply.github.com>
Signed-off-by: Gwena Cunha <4861122+gcunhase@users.noreply.github.com>
Co-authored-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
- Upgrade CICD test containers to latest
- Enable torch 2.10 testing in CICD
## Testing
<!-- Mention how have you tested your change if applicable. -->
CI/CD in this PR should pass
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added support for mixed-precision gradient handling with FSDP2.
* **Documentation**
* Updated Linux installation guide with CUDA 13.x support and cupy
dependency guidance.
* **Chores**
* Updated CI/CD workflows and test infrastructure to support PyTorch
2.10 and CUDA 13.
* Updated container image versions and test environment configurations.
* Updated TensorRT-LLM version requirements.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
**Type of change:** documentation <!-- Use one of the following: Bug
fix, new feature, new example, new tests, documentation. -->
**Overview:**
1. Added multi‑backbone support for quantization: --backbone now accepts
space- or comma-separated lists and resolves to a list of backbone
modules.
2. Introduced PipelineManager.iter_backbones() to iterate named backbone
modules and updated get_backbone() to return a single module or a
ModuleList for multi‑backbone.
3. Updated ExportManager to save/restore per‑backbone checkpoints when a
directory is provided, with {backbone_name}.pt files, and to create
target directories when missing.
4. Simplified save_checkpoint() calls to rely on the registered
pipeline_manager by default.
**Usage: **
```bash
python quantize.py --model wan2.2-t2v-14b --format fp4 --batch-size 1 --calib-size 32 \
--n-steps 30 --backbone transformer transformer_2 --model-dtype BFloat16 \
--quantized-torch-ckpt-save-path ./wan22_mo_ckpts \
--hf-ckpt-dir ./wan2.2-t2v-14b
```
Plans
- [x] [1/4] Add the basic functionalities to support limited image
models with NVFP4 + FP8, with some refactoring on the previous LLM code
and the diffusers example. PIC: @jingyu-ml
- [x] [2/4] Add support to more video gen models. PIC: @jingyu-ml
- [x] [3/4] Add test cases, refactor on the doc, and all related README.
PIC: @jingyu-ml
- [ ] [4/4] Add the final support to ComfyUI. PIC @jingyu-ml
## Testing
<!-- Mention how have you tested your change if applicable. -->
## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->
- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: No <!--- If No, explain why.
-->
- **Did you write any new necessary tests?**:No
- **Did you add or update any necessary documentation?**: Yes
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
## Additional Information
<!-- E.g. related issue. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Release Notes
* **New Features**
* Unified Hugging Face export support for diffusers pipelines and
components
* LTX-2 and Wan2.2 (T2V) support in diffusers quantization workflow
* Comprehensive ONNX export and TensorRT engine build documentation for
diffusion models
* **Documentation**
* Updated to clarify support for both transformers and diffusers models
in unified export API
* Expanded diffusers examples with LoRA fusion guidance and additional
model options (Flux, SD3, SDXL variants)
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Jingyu Xin <jingyux@nvidia.com>
## What does this PR do?
Documentation
**Overview:**
- Update support matrix, changelog, deployment page, example readmes as
per recent feature and model support on Windows side.
## Testing
- No testing, its just documentation change
## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->
- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes/No <!--- If No, explain
why. -->
- **Did you write any new necessary tests?**: Yes/No
- **Did you add or update any necessary documentation?**: Yes/No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes/No <!--- Only for new features, API changes, critical bug fixes or
bw breaking changes. -->
## Additional Information
<!-- E.g. related issue. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added ONNX Mixed Precision Weight-only quantization (INT4/INT8)
support.
* Introduced diffusion-model quantization on Windows.
* Added new accuracy benchmarks (Perplexity and KL-Divergence).
* Expanded deployment with multiple ONNX Runtime Execution Providers
(CUDA, DirectML, TensorRT-RTX).
* **Bug Fixes**
* Fixed ONNX 1.19 compatibility issue with CuPy during INT4 AWQ
quantization.
* **Documentation**
* Updated installation guides with system requirements and multiple
backend options.
* Reorganized deployment documentation with comprehensive execution
provider guidance.
* Expanded example workflows with improved setup instructions and
support matrices.
<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: vipandya <vipandya@nvidia.com>
- [x] Product Rename: TensorRT Model Optimizer to Model Optimizer
(OMNIML-3033)
- [x] Mention in Latest News section with date on the date of merging
this PR (12/08)
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
Bump TRT-LLM docker from 1.1.0rc2.post2 (CUDA 12) to 1.2.0rc4 (CUDA 13)
## Testing
<!-- Mention how have you tested your change if applicable. -->
- [ ] 2-gpu tests for all examples: ?
## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->
- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes <!--- If No, explain why.
-->
- **Did you write any new necessary tests?**: No
- **Did you add or update any necessary documentation?**: Yes
- **Did you update
[Changelog](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
vishalpandya1990