prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <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
  
  </issue>
  <code>
  [start of py/torch_tensorrt/dynamo/torch_compile/utils.py]
  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 of py/torch_tensorrt/dynamo/torch_compile/utils.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: 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
  
verification_info: {"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"}]}