We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN 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

--- 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/backend/backends.py`
Content:
```
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 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/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

{"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"}], "after_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=\"tensorrt\")  # type: ignore[misc]\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"}]}