We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Add Test Suite for `torch.compile` backend Partitioning/Lowering Phases
- Add robust test suite for `torch.compile` backend, ensuring each phase functions correctly
- Add general-purpose utilities for test expansion as the backend evolves

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `py/torch_tensorrt/dynamo/torch_compile/utils.py`
Content:
```
1 import torch
2 
3 from typing import Any, Union, Sequence, Dict
4 from torch_tensorrt import _Input, Device
5 
6 
7 def prepare_inputs(
8     inputs: Union[_Input.Input, torch.Tensor, Sequence, Dict],
9     device: torch.device = torch.device("cuda"),
10 ) -> Any:
11     if isinstance(inputs, _Input.Input):
12         if isinstance(inputs.shape, dict):
13             return inputs.example_tensor(optimization_profile_field="opt_shape").to(
14                 device
15             )
16         else:
17             return inputs.example_tensor().to(device)
18 
19     elif isinstance(inputs, torch.Tensor):
20         return inputs
21 
22     elif isinstance(inputs, list):
23         prepared_input = list()
24 
25         for input_obj in inputs:
26             prepared_input.append(prepare_inputs(input_obj))
27 
28         return prepared_input
29 
30     elif isinstance(inputs, tuple):
31         prepared_input = list()
32 
33         for input_obj in inputs:
34             prepared_input.append(prepare_inputs(input_obj))
35 
36         return tuple(prepared_input)
37 
38     elif isinstance(inputs, dict):
39         prepared_input = dict()
40 
41         for key, input_obj in inputs.items():
42             prepared_input[key] = prepare_inputs(input_obj)
43 
44         return prepared_input
45 
46     else:
47         raise ValueError(
48             f"Invalid input type {type(inputs)} encountered in the torch_compile input parsing. "
49             + "Allowed input types: {torch_tensorrt.Input, torch.Tensor, list, tuple, dict}"
50         )
51 
52 
53 def prepare_device(device: Union[Device, torch.device]) -> torch.device:
54     if isinstance(device, Device):
55         if device.gpu_id != -1:
56             device = torch.device(device.gpu_id)
57         else:
58             raise ValueError("Invalid GPU ID provided for the CUDA device provided")
59 
60     elif isinstance(device, torch.device):
61         device = device
62 
63     else:
64         raise ValueError(
65             "Invalid device provided. Supported options: torch.device | torch_tensorrt.Device"
66         )
67
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/py/torch_tensorrt/dynamo/torch_compile/utils.py b/py/torch_tensorrt/dynamo/torch_compile/utils.py
--- a/py/torch_tensorrt/dynamo/torch_compile/utils.py
+++ b/py/torch_tensorrt/dynamo/torch_compile/utils.py
@@ -64,3 +64,5 @@
         raise ValueError(
             "Invalid device provided. Supported options: torch.device | torch_tensorrt.Device"
         )
+
+    return device

{"golden_diff": "diff --git a/py/torch_tensorrt/dynamo/torch_compile/utils.py b/py/torch_tensorrt/dynamo/torch_compile/utils.py\n--- a/py/torch_tensorrt/dynamo/torch_compile/utils.py\n+++ b/py/torch_tensorrt/dynamo/torch_compile/utils.py\n@@ -64,3 +64,5 @@\n         raise ValueError(\n             \"Invalid device provided. Supported options: torch.device | torch_tensorrt.Device\"\n         )\n+\n+    return device\n", "issue": "Add Test Suite for `torch.compile` backend Partitioning/Lowering Phases\n- Add robust test suite for `torch.compile` backend, ensuring each phase functions correctly\r\n- Add general-purpose utilities for test expansion as the backend evolves\n", "before_files": [{"content": "import torch\n\nfrom typing import Any, Union, Sequence, Dict\nfrom torch_tensorrt import _Input, Device\n\n\ndef prepare_inputs(\n    inputs: Union[_Input.Input, torch.Tensor, Sequence, Dict],\n    device: torch.device = torch.device(\"cuda\"),\n) -> Any:\n    if isinstance(inputs, _Input.Input):\n        if isinstance(inputs.shape, dict):\n            return inputs.example_tensor(optimization_profile_field=\"opt_shape\").to(\n                device\n            )\n        else:\n            return inputs.example_tensor().to(device)\n\n    elif isinstance(inputs, torch.Tensor):\n        return inputs\n\n    elif isinstance(inputs, list):\n        prepared_input = list()\n\n        for input_obj in inputs:\n            prepared_input.append(prepare_inputs(input_obj))\n\n        return prepared_input\n\n    elif isinstance(inputs, tuple):\n        prepared_input = list()\n\n        for input_obj in inputs:\n            prepared_input.append(prepare_inputs(input_obj))\n\n        return tuple(prepared_input)\n\n    elif isinstance(inputs, dict):\n        prepared_input = dict()\n\n        for key, input_obj in inputs.items():\n            prepared_input[key] = prepare_inputs(input_obj)\n\n        return prepared_input\n\n    else:\n        raise ValueError(\n            f\"Invalid input type {type(inputs)} encountered in the torch_compile input parsing. \"\n            + \"Allowed input types: {torch_tensorrt.Input, torch.Tensor, list, tuple, dict}\"\n        )\n\n\ndef prepare_device(device: Union[Device, torch.device]) -> torch.device:\n    if isinstance(device, Device):\n        if device.gpu_id != -1:\n            device = torch.device(device.gpu_id)\n        else:\n            raise ValueError(\"Invalid GPU ID provided for the CUDA device provided\")\n\n    elif isinstance(device, torch.device):\n        device = device\n\n    else:\n        raise ValueError(\n            \"Invalid device provided. Supported options: torch.device | torch_tensorrt.Device\"\n        )\n", "path": "py/torch_tensorrt/dynamo/torch_compile/utils.py"}], "after_files": [{"content": "import torch\n\nfrom typing import Any, Union, Sequence, Dict\nfrom torch_tensorrt import _Input, Device\n\n\ndef prepare_inputs(\n    inputs: Union[_Input.Input, torch.Tensor, Sequence, Dict],\n    device: torch.device = torch.device(\"cuda\"),\n) -> Any:\n    if isinstance(inputs, _Input.Input):\n        if isinstance(inputs.shape, dict):\n            return inputs.example_tensor(optimization_profile_field=\"opt_shape\").to(\n                device\n            )\n        else:\n            return inputs.example_tensor().to(device)\n\n    elif isinstance(inputs, torch.Tensor):\n        return inputs\n\n    elif isinstance(inputs, list):\n        prepared_input = list()\n\n        for input_obj in inputs:\n            prepared_input.append(prepare_inputs(input_obj))\n\n        return prepared_input\n\n    elif isinstance(inputs, tuple):\n        prepared_input = list()\n\n        for input_obj in inputs:\n            prepared_input.append(prepare_inputs(input_obj))\n\n        return tuple(prepared_input)\n\n    elif isinstance(inputs, dict):\n        prepared_input = dict()\n\n        for key, input_obj in inputs.items():\n            prepared_input[key] = prepare_inputs(input_obj)\n\n        return prepared_input\n\n    else:\n        raise ValueError(\n            f\"Invalid input type {type(inputs)} encountered in the torch_compile input parsing. \"\n            + \"Allowed input types: {torch_tensorrt.Input, torch.Tensor, list, tuple, dict}\"\n        )\n\n\ndef prepare_device(device: Union[Device, torch.device]) -> torch.device:\n    if isinstance(device, Device):\n        if device.gpu_id != -1:\n            device = torch.device(device.gpu_id)\n        else:\n            raise ValueError(\"Invalid GPU ID provided for the CUDA device provided\")\n\n    elif isinstance(device, torch.device):\n        device = device\n\n    else:\n        raise ValueError(\n            \"Invalid device provided. Supported options: torch.device | torch_tensorrt.Device\"\n        )\n\n    return device\n", "path": "py/torch_tensorrt/dynamo/torch_compile/utils.py"}]}