Temporary fix to workaround the mutable decomposition error.

[lanluo-nvidia]

Description

Please include a summary of the change and which issue is fixed. Please also include relevant motivation and context. List any dependencies that are required for this change.

Fixes # (issue)

Type of change

Please delete options that are not relevant and/or add your own.

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

Checklist:

• My code follows the style guidelines of this project (You can use the linters)
• I have performed a self-review of my own code
• I have commented my code, particularly in hard-to-understand areas and hacks
• I have made corresponding changes to the documentation
• I have added tests to verify my fix or my feature
• New and existing unit tests pass locally with my changes
• I have added the relevant labels to my PR in so that relevant reviewers are notified

[cehongwang]

LGTM after Naren's comments

[github-actions]

There are some changes that do not conform to Python style guidelines:

--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/runtime/_MutableTorchTensorRTModule.py	2025-06-30 20:42:14.145185+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/runtime/_MutableTorchTensorRTModule.py	2025-06-30 20:42:41.576462+00:00
@@ -475,11 +475,13 @@
            f"Invalid input type {type(inputs)} encountered in the input. "
            + "Allowed input types: {torch_tensorrt.Input, torch.Tensor, list, tuple, dict}"
        )

    def forward(self, *args: Any, **kwargs: Any) -> Any:
-        warnings.warn("Direct calls to {self.__class__}.forward() are currently broken by due to https://github.com/pytorch/pytorch/issues/157183. Either call {self.__class__}(...) directly or use {self.__class__}._forward as a work around")
+        warnings.warn(
+            "Direct calls to {self.__class__}.forward() are currently broken by due to https://github.com/pytorch/pytorch/issues/157183. Either call {self.__class__}(...) directly or use {self.__class__}._forward as a work around"
+        )
        return self._forward(*args, **kwargs)

    def _forward(self, *args: Any, **kwargs: Any) -> Any:
        # Step 1: Check whether the input shape has changed
        kwargs = MutableTorchTensorRTModule._process_kwarg_inputs(kwargs)