🐛 [Bug] compile methods kwarg_inputs does not accept key values of "Any" type, despite documentation stating so

Bug Description

Passing a boolean value inside a dict to kwarg_inputs parameter of the torch_tensorrt.compile method results in

ValueError: Invalid input type <class 'bool'> encountered in the dynamo_compile input parsing. Allowed input types: {torch_tensorrt.Input, torch.Tensor, list, tuple, dict}

It seems that apart from collection types (list, tuple, dict), at leaf level only torch.Tensor values are allowed. This contradicts the documentation https://pytorch.org/TensorRT/py_api/torch_tensorrt.html?highlight=compile which states:

kwarg_inputs: Optional[dict[Any, Any]] = None

To Reproduce

Steps to reproduce the behavior:

1. Execute the following minimal example:

import torch
import torch_tensorrt

class TestModel(torch.nn.Module):
    def forward(self, param1, additional_param = bool | None):
        pass

compiled_model = torch_tensorrt.compile(
    TestModel(),
    ir="dynamo",
    inputs=[torch.rand(1)],
    kwarg_inputs={
        "additional_param": True
    },
)

2. The result is

Traceback (most recent call last):
  File "...\test_bug.py", line 8, in <module>
    compiled_model = torch_tensorrt.compile(
  File "...\lib\site-packages\torch_tensorrt\_compile.py", line 284, in compile
    torchtrt_kwarg_inputs = prepare_inputs(kwarg_inputs)
  File "...\lib\site-packages\torch_tensorrt\dynamo\utils.py", line 272, in prepare_inputs
    torchtrt_input = prepare_inputs(
  File "...\lib\site-packages\torch_tensorrt\dynamo\utils.py", line 280, in prepare_inputs
    raise ValueError(
ValueError: Invalid input type <class 'bool'> encountered in the dynamo_compile input parsing. Allowed input types: {torch_tensorrt.Input, torch.Tensor, list, tuple, dict}

Expected behavior

The minimal example should compile fine. Any values in addition to torch tensors in both - inputs and kwarg_inputs - should IMHO be accepted. It would additionally be nice if the documentation would be a bit more verbose about this IMHO important topic of how inputs will be treated by the compiler and what will happen at runtime of the compiled model.

Environment

Build information about Torch-TensorRT can be found by turning on debug messages

I am sorry, I do not know a canonical way of "turning on debug messages" in python. I do not know how this translates into something actionable.

• Torch-TensorRT Version (e.g. 1.0.0) / PyTorch Version (e.g. 1.0):

tensorrt==10.7.0
tensorrt_cu12==10.7.0
tensorrt_cu12_bindings==10.7.0
tensorrt_cu12_libs==10.7.0
torch==2.6.0+cu124
torch_tensorrt==2.6.0+cu124

• CPU Architecture: Intel x86_64
• OS (e.g., Linux): Windows 10
• How you installed PyTorch (conda, pip, libtorch, source): pip
• Python version: Python 3.10.16
• CUDA version: 12.4

Start with prepare_inputs in torch_tensorrt/dynamo/utils.py and follow its call from compile in torch_tensorrt/_compile.py. Reproduce the reported boolean kwarg_inputs case, then determine the supported leaf-value behavior and verify completion with a regression test or clarified documentation.