We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Fix import error for Legacy TorchScript CI on `TorchTensorRTModule`
- Older versions of TorchScript do not have the `torch._dynamo` import capability

--- 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/fx/tracer/dispatch_tracer/aten_tracer.py`
Content:
```
1 import copy
2 import sys
3 from contextlib import contextmanager
4 from typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Union
5 
6 import torch
7 
8 if not torch.__version__.startswith("1"):
9     import torch._dynamo as torchdynamo
10 
11 from torch.fx.passes.infra.pass_base import PassResult
12 from torch_tensorrt.fx.utils import req_torch_version
13 from torch_tensorrt.fx.passes.lower_basic_pass_aten import (
14     compose_bmm,
15     compose_chunk,
16     compose_getitem_slice,
17     remove_ops,
18     replace_aten_op_with_indices,
19     replace_aten_reshape_alias_with_replace,
20     replace_builtin_ops,
21     replace_inplace_ops,
22     replace_native_layernorm_with_layernorm,
23     replace_transpose_mm_op_with_linear,
24     run_const_fold,
25 )
26 from typing_extensions import TypeAlias
27 
28 Value: TypeAlias = Union[
29     Tuple["Value", ...],
30     List["Value"],
31     Dict[str, "Value"],
32 ]
33 
34 
35 class DynamoConfig:
36     """
37     Manage Exir-specific configurations of Dynamo.
38     """
39 
40     def __init__(
41         self,
42         capture_scalar_outputs: bool = True,
43         guard_nn_modules: bool = True,
44         dynamic_shapes: bool = True,
45         specialize_int: bool = True,
46         verbose: bool = True,
47     ) -> None:
48 
49         self.capture_scalar_outputs = capture_scalar_outputs
50         self.guard_nn_modules = guard_nn_modules
51         self.dynamic_shapes = dynamic_shapes
52         self.specialize_int = specialize_int
53         self.verbose = verbose
54 
55     def activate(self) -> None:
56         torchdynamo.config.capture_scalar_outputs = self.capture_scalar_outputs
57         torchdynamo.config.guard_nn_modules = self.guard_nn_modules
58         torchdynamo.config.dynamic_shapes = self.dynamic_shapes
59         torchdynamo.config.specialize_int = self.specialize_int
60         torchdynamo.config.verbose = self.verbose
61 
62     def deactivate(self) -> None:
63         torchdynamo.config.capture_scalar_outputs = True
64         torchdynamo.config.guard_nn_modules = True
65         torchdynamo.config.dynamic_shapes = True
66         torchdynamo.config.specialize_int = True
67         torchdynamo.config.verbose = True
68 
69 
70 @contextmanager
71 def using_config(config: DynamoConfig) -> Generator[DynamoConfig, None, None]:
72     config.activate()
73     try:
74         yield config
75     finally:
76         config.deactivate()
77 
78 
79 @contextmanager
80 def setting_python_recursive_limit(limit: int = 10000) -> Generator[None, None, None]:
81     """
82     Temporarily increase the python interpreter stack recursion limit.
83     This is mostly used for pickling large scale modules.
84     """
85     default = sys.getrecursionlimit()
86     if limit > default:
87         sys.setrecursionlimit(limit)
88     try:
89         yield
90     finally:
91         sys.setrecursionlimit(default)
92 
93 
94 @req_torch_version("2.dev")
95 def dynamo_trace(
96     f: Callable[..., Value],
97     # pyre-ignore
98     args: Tuple[Any, ...],
99     aten_graph: bool,
100     tracing_mode: str = "real",
101     dynamo_config: Optional[DynamoConfig] = None,
102 ) -> Tuple[torch.fx.GraphModule, Set]:
103     """
104     TODO: Once we fully migrate to torchdynamo frontend, we will remove
105     this config option alltogether.  For now, it helps with quick
106     experiments with playing around with TorchDynamo
107     """
108     if dynamo_config is None:
109         dynamo_config = DynamoConfig()
110     with using_config(dynamo_config), setting_python_recursive_limit(2000):
111         torchdynamo.reset()
112         try:
113             return torchdynamo.export(
114                 f,
115                 *copy.deepcopy(args),
116                 aten_graph=aten_graph,
117                 tracing_mode=tracing_mode,
118             )
119         except torchdynamo.exc.Unsupported as exc:
120             raise RuntimeError(
121                 "The user code is using a feature we don't support. "
122                 "Please try torchdynamo.explain() to get possible the reasons",
123             ) from exc
124         except Exception as exc:
125             raise RuntimeError(
126                 "torchdynamo internal error occured. Please see above stacktrace"
127             ) from exc
128 
129 
130 @req_torch_version("2.dev")
131 def trace(f, args, *rest):
132     graph_module, guards = dynamo_trace(f, args, True, "symbolic")
133     return graph_module, guards
134 
135 
136 @req_torch_version("2.dev")
137 def opt_trace(f, args, *rest):
138     """
139     Optimized trace with necessary passes which re-compose some ops or replace some ops
140     These passes should be general and functional purpose
141     """
142     passes_list = [
143         compose_bmm,
144         compose_chunk,
145         compose_getitem_slice,
146         replace_aten_reshape_alias_with_replace,
147         replace_aten_op_with_indices,
148         replace_transpose_mm_op_with_linear,  # after compose_bmm
149         replace_native_layernorm_with_layernorm,
150         remove_ops,
151         replace_builtin_ops,  # after replace_native_layernorm_with_layernorm
152         replace_inplace_ops,  # remove it once functionalization is enabled
153     ]
154 
155     fx_module, _ = trace(f, args)
156     print(fx_module.graph)
157     for passes in passes_list:
158         pr: PassResult = passes(fx_module)
159         fx_module = pr.graph_module
160 
161     fx_module(*args)
162 
163     fx_module = run_const_fold(fx_module)
164     print(fx_module.graph)
165     return fx_module
166
```

Path: `py/torch_tensorrt/dynamo/__init__.py`
Content:
```
1 from torch_tensorrt.dynamo import fx_ts_compat
2 from .backend import compile
3
```
--- 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/__init__.py b/py/torch_tensorrt/dynamo/__init__.py
--- a/py/torch_tensorrt/dynamo/__init__.py
+++ b/py/torch_tensorrt/dynamo/__init__.py
@@ -1,2 +1,6 @@
-from torch_tensorrt.dynamo import fx_ts_compat
-from .backend import compile
+import torch
+from packaging import version
+
+if version.parse(torch.__version__) >= version.parse("2.1.dev"):
+    from torch_tensorrt.dynamo import fx_ts_compat
+    from .backend import compile
diff --git a/py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py b/py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py
--- a/py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py
+++ b/py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py
@@ -2,10 +2,11 @@
 import sys
 from contextlib import contextmanager
 from typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Union
+from packaging import version
 
 import torch
 
-if not torch.__version__.startswith("1"):
+if version.parse(torch.__version__) >= version.parse("2.dev"):
     import torch._dynamo as torchdynamo
 
 from torch.fx.passes.infra.pass_base import PassResult

{"golden_diff": "diff --git a/py/torch_tensorrt/dynamo/__init__.py b/py/torch_tensorrt/dynamo/__init__.py\n--- a/py/torch_tensorrt/dynamo/__init__.py\n+++ b/py/torch_tensorrt/dynamo/__init__.py\n@@ -1,2 +1,6 @@\n-from torch_tensorrt.dynamo import fx_ts_compat\n-from .backend import compile\n+import torch\n+from packaging import version\n+\n+if version.parse(torch.__version__) >= version.parse(\"2.1.dev\"):\n+    from torch_tensorrt.dynamo import fx_ts_compat\n+    from .backend import compile\ndiff --git a/py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py b/py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py\n--- a/py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py\n+++ b/py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py\n@@ -2,10 +2,11 @@\n import sys\n from contextlib import contextmanager\n from typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Union\n+from packaging import version\n \n import torch\n \n-if not torch.__version__.startswith(\"1\"):\n+if version.parse(torch.__version__) >= version.parse(\"2.dev\"):\n     import torch._dynamo as torchdynamo\n \n from torch.fx.passes.infra.pass_base import PassResult\n", "issue": "Fix import error for Legacy TorchScript CI on `TorchTensorRTModule`\n- Older versions of TorchScript do not have the `torch._dynamo` import capability\n", "before_files": [{"content": "import copy\nimport sys\nfrom contextlib import contextmanager\nfrom typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Union\n\nimport torch\n\nif not torch.__version__.startswith(\"1\"):\n    import torch._dynamo as torchdynamo\n\nfrom torch.fx.passes.infra.pass_base import PassResult\nfrom torch_tensorrt.fx.utils import req_torch_version\nfrom torch_tensorrt.fx.passes.lower_basic_pass_aten import (\n    compose_bmm,\n    compose_chunk,\n    compose_getitem_slice,\n    remove_ops,\n    replace_aten_op_with_indices,\n    replace_aten_reshape_alias_with_replace,\n    replace_builtin_ops,\n    replace_inplace_ops,\n    replace_native_layernorm_with_layernorm,\n    replace_transpose_mm_op_with_linear,\n    run_const_fold,\n)\nfrom typing_extensions import TypeAlias\n\nValue: TypeAlias = Union[\n    Tuple[\"Value\", ...],\n    List[\"Value\"],\n    Dict[str, \"Value\"],\n]\n\n\nclass DynamoConfig:\n    \"\"\"\n    Manage Exir-specific configurations of Dynamo.\n    \"\"\"\n\n    def __init__(\n        self,\n        capture_scalar_outputs: bool = True,\n        guard_nn_modules: bool = True,\n        dynamic_shapes: bool = True,\n        specialize_int: bool = True,\n        verbose: bool = True,\n    ) -> None:\n\n        self.capture_scalar_outputs = capture_scalar_outputs\n        self.guard_nn_modules = guard_nn_modules\n        self.dynamic_shapes = dynamic_shapes\n        self.specialize_int = specialize_int\n        self.verbose = verbose\n\n    def activate(self) -> None:\n        torchdynamo.config.capture_scalar_outputs = self.capture_scalar_outputs\n        torchdynamo.config.guard_nn_modules = self.guard_nn_modules\n        torchdynamo.config.dynamic_shapes = self.dynamic_shapes\n        torchdynamo.config.specialize_int = self.specialize_int\n        torchdynamo.config.verbose = self.verbose\n\n    def deactivate(self) -> None:\n        torchdynamo.config.capture_scalar_outputs = True\n        torchdynamo.config.guard_nn_modules = True\n        torchdynamo.config.dynamic_shapes = True\n        torchdynamo.config.specialize_int = True\n        torchdynamo.config.verbose = True\n\n\n@contextmanager\ndef using_config(config: DynamoConfig) -> Generator[DynamoConfig, None, None]:\n    config.activate()\n    try:\n        yield config\n    finally:\n        config.deactivate()\n\n\n@contextmanager\ndef setting_python_recursive_limit(limit: int = 10000) -> Generator[None, None, None]:\n    \"\"\"\n    Temporarily increase the python interpreter stack recursion limit.\n    This is mostly used for pickling large scale modules.\n    \"\"\"\n    default = sys.getrecursionlimit()\n    if limit > default:\n        sys.setrecursionlimit(limit)\n    try:\n        yield\n    finally:\n        sys.setrecursionlimit(default)\n\n\n@req_torch_version(\"2.dev\")\ndef dynamo_trace(\n    f: Callable[..., Value],\n    # pyre-ignore\n    args: Tuple[Any, ...],\n    aten_graph: bool,\n    tracing_mode: str = \"real\",\n    dynamo_config: Optional[DynamoConfig] = None,\n) -> Tuple[torch.fx.GraphModule, Set]:\n    \"\"\"\n    TODO: Once we fully migrate to torchdynamo frontend, we will remove\n    this config option alltogether.  For now, it helps with quick\n    experiments with playing around with TorchDynamo\n    \"\"\"\n    if dynamo_config is None:\n        dynamo_config = DynamoConfig()\n    with using_config(dynamo_config), setting_python_recursive_limit(2000):\n        torchdynamo.reset()\n        try:\n            return torchdynamo.export(\n                f,\n                *copy.deepcopy(args),\n                aten_graph=aten_graph,\n                tracing_mode=tracing_mode,\n            )\n        except torchdynamo.exc.Unsupported as exc:\n            raise RuntimeError(\n                \"The user code is using a feature we don't support. \"\n                \"Please try torchdynamo.explain() to get possible the reasons\",\n            ) from exc\n        except Exception as exc:\n            raise RuntimeError(\n                \"torchdynamo internal error occured. Please see above stacktrace\"\n            ) from exc\n\n\n@req_torch_version(\"2.dev\")\ndef trace(f, args, *rest):\n    graph_module, guards = dynamo_trace(f, args, True, \"symbolic\")\n    return graph_module, guards\n\n\n@req_torch_version(\"2.dev\")\ndef opt_trace(f, args, *rest):\n    \"\"\"\n    Optimized trace with necessary passes which re-compose some ops or replace some ops\n    These passes should be general and functional purpose\n    \"\"\"\n    passes_list = [\n        compose_bmm,\n        compose_chunk,\n        compose_getitem_slice,\n        replace_aten_reshape_alias_with_replace,\n        replace_aten_op_with_indices,\n        replace_transpose_mm_op_with_linear,  # after compose_bmm\n        replace_native_layernorm_with_layernorm,\n        remove_ops,\n        replace_builtin_ops,  # after replace_native_layernorm_with_layernorm\n        replace_inplace_ops,  # remove it once functionalization is enabled\n    ]\n\n    fx_module, _ = trace(f, args)\n    print(fx_module.graph)\n    for passes in passes_list:\n        pr: PassResult = passes(fx_module)\n        fx_module = pr.graph_module\n\n    fx_module(*args)\n\n    fx_module = run_const_fold(fx_module)\n    print(fx_module.graph)\n    return fx_module\n", "path": "py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py"}, {"content": "from torch_tensorrt.dynamo import fx_ts_compat\nfrom .backend import compile\n", "path": "py/torch_tensorrt/dynamo/__init__.py"}], "after_files": [{"content": "import copy\nimport sys\nfrom contextlib import contextmanager\nfrom typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Union\nfrom packaging import version\n\nimport torch\n\nif version.parse(torch.__version__) >= version.parse(\"2.dev\"):\n    import torch._dynamo as torchdynamo\n\nfrom torch.fx.passes.infra.pass_base import PassResult\nfrom torch_tensorrt.fx.utils import req_torch_version\nfrom torch_tensorrt.fx.passes.lower_basic_pass_aten import (\n    compose_bmm,\n    compose_chunk,\n    compose_getitem_slice,\n    remove_ops,\n    replace_aten_op_with_indices,\n    replace_aten_reshape_alias_with_replace,\n    replace_builtin_ops,\n    replace_inplace_ops,\n    replace_native_layernorm_with_layernorm,\n    replace_transpose_mm_op_with_linear,\n    run_const_fold,\n)\nfrom typing_extensions import TypeAlias\n\nValue: TypeAlias = Union[\n    Tuple[\"Value\", ...],\n    List[\"Value\"],\n    Dict[str, \"Value\"],\n]\n\n\nclass DynamoConfig:\n    \"\"\"\n    Manage Exir-specific configurations of Dynamo.\n    \"\"\"\n\n    def __init__(\n        self,\n        capture_scalar_outputs: bool = True,\n        guard_nn_modules: bool = True,\n        dynamic_shapes: bool = True,\n        specialize_int: bool = True,\n        verbose: bool = True,\n    ) -> None:\n\n        self.capture_scalar_outputs = capture_scalar_outputs\n        self.guard_nn_modules = guard_nn_modules\n        self.dynamic_shapes = dynamic_shapes\n        self.specialize_int = specialize_int\n        self.verbose = verbose\n\n    def activate(self) -> None:\n        torchdynamo.config.capture_scalar_outputs = self.capture_scalar_outputs\n        torchdynamo.config.guard_nn_modules = self.guard_nn_modules\n        torchdynamo.config.dynamic_shapes = self.dynamic_shapes\n        torchdynamo.config.specialize_int = self.specialize_int\n        torchdynamo.config.verbose = self.verbose\n\n    def deactivate(self) -> None:\n        torchdynamo.config.capture_scalar_outputs = True\n        torchdynamo.config.guard_nn_modules = True\n        torchdynamo.config.dynamic_shapes = True\n        torchdynamo.config.specialize_int = True\n        torchdynamo.config.verbose = True\n\n\n@contextmanager\ndef using_config(config: DynamoConfig) -> Generator[DynamoConfig, None, None]:\n    config.activate()\n    try:\n        yield config\n    finally:\n        config.deactivate()\n\n\n@contextmanager\ndef setting_python_recursive_limit(limit: int = 10000) -> Generator[None, None, None]:\n    \"\"\"\n    Temporarily increase the python interpreter stack recursion limit.\n    This is mostly used for pickling large scale modules.\n    \"\"\"\n    default = sys.getrecursionlimit()\n    if limit > default:\n        sys.setrecursionlimit(limit)\n    try:\n        yield\n    finally:\n        sys.setrecursionlimit(default)\n\n\n@req_torch_version(\"2.dev\")\ndef dynamo_trace(\n    f: Callable[..., Value],\n    # pyre-ignore\n    args: Tuple[Any, ...],\n    aten_graph: bool,\n    tracing_mode: str = \"real\",\n    dynamo_config: Optional[DynamoConfig] = None,\n) -> Tuple[torch.fx.GraphModule, Set]:\n    \"\"\"\n    TODO: Once we fully migrate to torchdynamo frontend, we will remove\n    this config option alltogether.  For now, it helps with quick\n    experiments with playing around with TorchDynamo\n    \"\"\"\n    if dynamo_config is None:\n        dynamo_config = DynamoConfig()\n    with using_config(dynamo_config), setting_python_recursive_limit(2000):\n        torchdynamo.reset()\n        try:\n            return torchdynamo.export(\n                f,\n                *copy.deepcopy(args),\n                aten_graph=aten_graph,\n                tracing_mode=tracing_mode,\n            )\n        except torchdynamo.exc.Unsupported as exc:\n            raise RuntimeError(\n                \"The user code is using a feature we don't support. \"\n                \"Please try torchdynamo.explain() to get possible the reasons\",\n            ) from exc\n        except Exception as exc:\n            raise RuntimeError(\n                \"torchdynamo internal error occured. Please see above stacktrace\"\n            ) from exc\n\n\n@req_torch_version(\"2.dev\")\ndef trace(f, args, *rest):\n    graph_module, guards = dynamo_trace(f, args, True, \"symbolic\")\n    return graph_module, guards\n\n\n@req_torch_version(\"2.dev\")\ndef opt_trace(f, args, *rest):\n    \"\"\"\n    Optimized trace with necessary passes which re-compose some ops or replace some ops\n    These passes should be general and functional purpose\n    \"\"\"\n    passes_list = [\n        compose_bmm,\n        compose_chunk,\n        compose_getitem_slice,\n        replace_aten_reshape_alias_with_replace,\n        replace_aten_op_with_indices,\n        replace_transpose_mm_op_with_linear,  # after compose_bmm\n        replace_native_layernorm_with_layernorm,\n        remove_ops,\n        replace_builtin_ops,  # after replace_native_layernorm_with_layernorm\n        replace_inplace_ops,  # remove it once functionalization is enabled\n    ]\n\n    fx_module, _ = trace(f, args)\n    print(fx_module.graph)\n    for passes in passes_list:\n        pr: PassResult = passes(fx_module)\n        fx_module = pr.graph_module\n\n    fx_module(*args)\n\n    fx_module = run_const_fold(fx_module)\n    print(fx_module.graph)\n    return fx_module\n", "path": "py/torch_tensorrt/fx/tracer/dispatch_tracer/aten_tracer.py"}, {"content": "import torch\nfrom packaging import version\n\nif version.parse(torch.__version__) >= version.parse(\"2.1.dev\"):\n    from torch_tensorrt.dynamo import fx_ts_compat\n    from .backend import compile\n", "path": "py/torch_tensorrt/dynamo/__init__.py"}]}