Changes to TRT-LLM download tool for multigpu distributed case

[apbose]

TRT-LLM installation tool for distributed

1. The download is to be done by only one GPU to avoid unnecessary downloads
2. Use of barrier in the tool for the above purpose
3. The util functions for TRT-LLM installation is moved to dynamo/distributed/utils.py with the initialization and cleanup of the env to be done on user side. (We do it in tests/py/dynamo/distributed and examples/distributed_inference/tensor_parallel_initialize_dist)

[github-actions]

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

--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/utils.py	2025-09-22 06:35:28.523784+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/utils.py	2025-09-22 06:36:00.657186+00:00
@@ -863,6 +863,6 @@
    return False


def is_thor() -> bool:
    if torch.cuda.get_device_capability() in [(11, 0)]:
-        return True
\ No newline at end of file
+        return True

[github-actions]

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

--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/distributed/utils.py	2025-09-25 19:33:28.176615+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/distributed/utils.py	2025-09-25 19:34:02.325958+00:00
@@ -100,11 +100,10 @@
        return True

    except Exception as e:
        logger.warning(f"Failed to detect CUDA version: {e}")
        return False
-

    return True


def _cache_root() -> Path: