prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Upstream Dynamo Backend to Torch
  - Add a hook in the Torch repo to secure the namespace `"tensorrt"` and have it point to `"torch_tensorrt"`
  - Add necessary imports and skipped tests
  - Raise a PR in Torch to add this functionality
  
  </issue>
  <code>
  [start of py/torch_tensorrt/dynamo/backend/backends.py]
  1 from __future__ import annotations
  2 
  3 import logging
  4 from functools import partial
  5 from typing import Any, Callable, Sequence
  6 
  7 import torch
  8 import torch._dynamo as td
  9 from torch._functorch.aot_autograd import aot_module_simplified, make_boxed_compiler
  10 from torch_tensorrt.dynamo import CompilationSettings
  11 from torch_tensorrt.dynamo.compile import compile_module
  12 from torch_tensorrt.dynamo.lowering._decompositions import get_decompositions
  13 from torch_tensorrt.dynamo.lowering._pre_aot_lowering import pre_aot_substitutions
  14 from torch_tensorrt.dynamo.utils import parse_dynamo_kwargs
  15 
  16 logger = logging.getLogger(__name__)
  17 
  18 
  19 @td.register_backend(name="torch_tensorrt")  # type: ignore[misc]
  20 def torch_tensorrt_backend(
  21     gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor], **kwargs: Any
  22 ) -> torch.nn.Module:
  23     # Set log level at the top of compilation (torch_tensorrt.dynamo)
  24     if (
  25         (
  26             "options" in kwargs
  27             and "debug" in kwargs["options"]
  28             and kwargs["options"]["debug"]
  29         )
  30         or ("debug" in kwargs and kwargs["debug"])
  31     ) and logger.parent:
  32         logger.parent.setLevel(logging.DEBUG)
  33 
  34     DEFAULT_BACKEND = aot_torch_tensorrt_aten_backend
  35 
  36     compiled_mod: torch.nn.Module = DEFAULT_BACKEND(gm, sample_inputs, **kwargs)
  37     return compiled_mod
  38 
  39 
  40 @td.register_backend(name="aot_torch_tensorrt_aten")  # type: ignore[misc]
  41 def aot_torch_tensorrt_aten_backend(
  42     gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor], **kwargs: Any
  43 ) -> torch.nn.Module:
  44     settings = parse_dynamo_kwargs(kwargs)
  45 
  46     custom_backend = partial(
  47         _pretraced_backend,
  48         settings=settings,
  49     )
  50 
  51     # Perform Pre-AOT Lowering for Module-Level Replacement
  52     gm = pre_aot_substitutions(gm)
  53 
  54     # Invoke AOTAutograd to translate operators to aten
  55     return aot_module_simplified(
  56         gm,
  57         sample_inputs,
  58         fw_compiler=make_boxed_compiler(custom_backend),
  59         decompositions=get_decompositions(settings.enable_experimental_decompositions),
  60     )
  61 
  62 
  63 def _pretraced_backend(
  64     gm: torch.fx.GraphModule,
  65     sample_inputs: Sequence[torch.Tensor],
  66     settings: CompilationSettings = CompilationSettings(),
  67 ) -> torch.fx.GraphModule | Callable[..., Any]:
  68     """Helper function to manage translation of traced FX module to TRT engines
  69 
  70     Args:
  71         module: FX GraphModule to convert
  72         inputs: Inputs to the module
  73         settings: Compilation settings
  74     Returns:
  75         Compiled FX GraphModule
  76     """
  77     try:
  78         logger.debug("Post-AOT Autograd graph:\n" + str(gm.graph))
  79 
  80         trt_compiled = compile_module(
  81             gm,
  82             sample_inputs,
  83             settings=settings,
  84         )
  85         return trt_compiled
  86     except AssertionError:
  87         if not settings.pass_through_build_failures:
  88             logger.warning(
  89                 "TRT conversion failed on the subgraph. See trace above. "
  90                 + "Returning GraphModule forward instead.",
  91                 exc_info=True,
  92             )
  93             return gm.forward
  94         else:
  95             logger.critical(
  96                 "Halting compilation on build failure since "
  97                 + "pass_through_build_failures was specified as True. "
  98                 + "To return the default Torch implementation and avoid "
  99                 + "halting compilation on engine build failures, "
  100                 + "specify pass_through_build_failures=False."
  101             )
  102             raise
  103
  [end of py/torch_tensorrt/dynamo/backend/backends.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/backend/backends.py b/py/torch_tensorrt/dynamo/backend/backends.py
  --- a/py/torch_tensorrt/dynamo/backend/backends.py
  +++ b/py/torch_tensorrt/dynamo/backend/backends.py
  @@ -16,6 +16,7 @@
   logger = logging.getLogger(__name__)
   
   
  +@td.register_backend(name="tensorrt")  # type: ignore[misc]
   @td.register_backend(name="torch_tensorrt")  # type: ignore[misc]
   def torch_tensorrt_backend(
       gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor], **kwargs: Any
  
verification_info: {"golden_diff": "diff --git a/py/torch_tensorrt/dynamo/backend/backends.py b/py/torch_tensorrt/dynamo/backend/backends.py\n--- a/py/torch_tensorrt/dynamo/backend/backends.py\n+++ b/py/torch_tensorrt/dynamo/backend/backends.py\n@@ -16,6 +16,7 @@\n logger = logging.getLogger(__name__)\n \n \n+@td.register_backend(name=\"tensorrt\")  # type: ignore[misc]\n @td.register_backend(name=\"torch_tensorrt\")  # type: ignore[misc]\n def torch_tensorrt_backend(\n     gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor], **kwargs: Any\n", "issue": "Upstream Dynamo Backend to Torch\n- Add a hook in the Torch repo to secure the namespace `\"tensorrt\"` and have it point to `\"torch_tensorrt\"`\r\n- Add necessary imports and skipped tests\r\n- Raise a PR in Torch to add this functionality\n", "before_files": [{"content": "from __future__ import annotations\n\nimport logging\nfrom functools import partial\nfrom typing import Any, Callable, Sequence\n\nimport torch\nimport torch._dynamo as td\nfrom torch._functorch.aot_autograd import aot_module_simplified, make_boxed_compiler\nfrom torch_tensorrt.dynamo import CompilationSettings\nfrom torch_tensorrt.dynamo.compile import compile_module\nfrom torch_tensorrt.dynamo.lowering._decompositions import get_decompositions\nfrom torch_tensorrt.dynamo.lowering._pre_aot_lowering import pre_aot_substitutions\nfrom torch_tensorrt.dynamo.utils import parse_dynamo_kwargs\n\nlogger = logging.getLogger(__name__)\n\n\n@td.register_backend(name=\"torch_tensorrt\")  # type: ignore[misc]\ndef torch_tensorrt_backend(\n    gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor], **kwargs: Any\n) -> torch.nn.Module:\n    # Set log level at the top of compilation (torch_tensorrt.dynamo)\n    if (\n        (\n            \"options\" in kwargs\n            and \"debug\" in kwargs[\"options\"]\n            and kwargs[\"options\"][\"debug\"]\n        )\n        or (\"debug\" in kwargs and kwargs[\"debug\"])\n    ) and logger.parent:\n        logger.parent.setLevel(logging.DEBUG)\n\n    DEFAULT_BACKEND = aot_torch_tensorrt_aten_backend\n\n    compiled_mod: torch.nn.Module = DEFAULT_BACKEND(gm, sample_inputs, **kwargs)\n    return compiled_mod\n\n\n@td.register_backend(name=\"aot_torch_tensorrt_aten\")  # type: ignore[misc]\ndef aot_torch_tensorrt_aten_backend(\n    gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor], **kwargs: Any\n) -> torch.nn.Module:\n    settings = parse_dynamo_kwargs(kwargs)\n\n    custom_backend = partial(\n        _pretraced_backend,\n        settings=settings,\n    )\n\n    # Perform Pre-AOT Lowering for Module-Level Replacement\n    gm = pre_aot_substitutions(gm)\n\n    # Invoke AOTAutograd to translate operators to aten\n    return aot_module_simplified(\n        gm,\n        sample_inputs,\n        fw_compiler=make_boxed_compiler(custom_backend),\n        decompositions=get_decompositions(settings.enable_experimental_decompositions),\n    )\n\n\ndef _pretraced_backend(\n    gm: torch.fx.GraphModule,\n    sample_inputs: Sequence[torch.Tensor],\n    settings: CompilationSettings = CompilationSettings(),\n) -> torch.fx.GraphModule | Callable[..., Any]:\n    \"\"\"Helper function to manage translation of traced FX module to TRT engines\n\n    Args:\n        module: FX GraphModule to convert\n        inputs: Inputs to the module\n        settings: Compilation settings\n    Returns:\n        Compiled FX GraphModule\n    \"\"\"\n    try:\n        logger.debug(\"Post-AOT Autograd graph:\\n\" + str(gm.graph))\n\n        trt_compiled = compile_module(\n            gm,\n            sample_inputs,\n            settings=settings,\n        )\n        return trt_compiled\n    except AssertionError:\n        if not settings.pass_through_build_failures:\n            logger.warning(\n                \"TRT conversion failed on the subgraph. See trace above. \"\n                + \"Returning GraphModule forward instead.\",\n                exc_info=True,\n            )\n            return gm.forward\n        else:\n            logger.critical(\n                \"Halting compilation on build failure since \"\n                + \"pass_through_build_failures was specified as True. \"\n                + \"To return the default Torch implementation and avoid \"\n                + \"halting compilation on engine build failures, \"\n                + \"specify pass_through_build_failures=False.\"\n            )\n            raise\n", "path": "py/torch_tensorrt/dynamo/backend/backends.py"}]}