We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
`aten.arange.start_step`


--- 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/conversion/ops_evaluators.py`
Content:
```
1 import logging
2 import operator
3 from typing import Dict, Sequence, Tuple, Union
4 
5 import torch
6 from torch.fx.node import Argument, Node, Target
7 from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
8 from torch_tensorrt.dynamo.conversion._ConverterRegistry import (
9     ConverterRegistry,
10     dynamo_tensorrt_converter,
11 )
12 from torch_tensorrt.fx.types import TRTTensor
13 
14 _LOGGER: logging.Logger = logging.getLogger(__name__)
15 
16 
17 def getitem_validator(getitem_node: Node) -> bool:
18     from torch_tensorrt.dynamo.conversion._ConverterRegistry import DYNAMO_CONVERTERS
19 
20     # Getitem nodes can only be converted if their parent node also can
21     return getitem_node.args[0] in DYNAMO_CONVERTERS
22 
23 
24 # TODO: Subsequent evaluators should be registered here with their own validators
25 @dynamo_tensorrt_converter(operator.getitem, capability_validator=getitem_validator)
26 @dynamo_tensorrt_converter(torch.ops.aten.detach.default)
27 def generic_evaluator(
28     ctx: ConversionContext,
29     target: Target,
30     args: Tuple[Argument, ...],
31     kwargs: Dict[str, Argument],
32     name: str,
33 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
34     _LOGGER.debug(
35         f"Evaluating {ConverterRegistry.qualified_name_or_str(target)} on object with name: {name}"
36     )
37     return target(*args)
38
```
--- 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/conversion/ops_evaluators.py b/py/torch_tensorrt/dynamo/conversion/ops_evaluators.py
--- a/py/torch_tensorrt/dynamo/conversion/ops_evaluators.py
+++ b/py/torch_tensorrt/dynamo/conversion/ops_evaluators.py
@@ -2,6 +2,7 @@
 import operator
 from typing import Dict, Sequence, Tuple, Union
 
+import numpy as np
 import torch
 from torch.fx.node import Argument, Node, Target
 from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
@@ -35,3 +36,14 @@
         f"Evaluating {ConverterRegistry.qualified_name_or_str(target)} on object with name: {name}"
     )
     return target(*args)
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.arange.start_step)
+def aten_ops_arange_start_step(
+    ctx: ConversionContext,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return np.arange(*args)

{"golden_diff": "diff --git a/py/torch_tensorrt/dynamo/conversion/ops_evaluators.py b/py/torch_tensorrt/dynamo/conversion/ops_evaluators.py\n--- a/py/torch_tensorrt/dynamo/conversion/ops_evaluators.py\n+++ b/py/torch_tensorrt/dynamo/conversion/ops_evaluators.py\n@@ -2,6 +2,7 @@\n import operator\n from typing import Dict, Sequence, Tuple, Union\n \n+import numpy as np\n import torch\n from torch.fx.node import Argument, Node, Target\n from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext\n@@ -35,3 +36,14 @@\n         f\"Evaluating {ConverterRegistry.qualified_name_or_str(target)} on object with name: {name}\"\n     )\n     return target(*args)\n+\n+\n+@dynamo_tensorrt_converter(torch.ops.aten.arange.start_step)\n+def aten_ops_arange_start_step(\n+    ctx: ConversionContext,\n+    target: Target,\n+    args: Tuple[Argument, ...],\n+    kwargs: Dict[str, Argument],\n+    name: str,\n+) -> Union[TRTTensor, Sequence[TRTTensor]]:\n+    return np.arange(*args)\n", "issue": "`aten.arange.start_step`\n\n", "before_files": [{"content": "import logging\nimport operator\nfrom typing import Dict, Sequence, Tuple, Union\n\nimport torch\nfrom torch.fx.node import Argument, Node, Target\nfrom torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext\nfrom torch_tensorrt.dynamo.conversion._ConverterRegistry import (\n    ConverterRegistry,\n    dynamo_tensorrt_converter,\n)\nfrom torch_tensorrt.fx.types import TRTTensor\n\n_LOGGER: logging.Logger = logging.getLogger(__name__)\n\n\ndef getitem_validator(getitem_node: Node) -> bool:\n    from torch_tensorrt.dynamo.conversion._ConverterRegistry import DYNAMO_CONVERTERS\n\n    # Getitem nodes can only be converted if their parent node also can\n    return getitem_node.args[0] in DYNAMO_CONVERTERS\n\n\n# TODO: Subsequent evaluators should be registered here with their own validators\n@dynamo_tensorrt_converter(operator.getitem, capability_validator=getitem_validator)\n@dynamo_tensorrt_converter(torch.ops.aten.detach.default)\ndef generic_evaluator(\n    ctx: ConversionContext,\n    target: Target,\n    args: Tuple[Argument, ...],\n    kwargs: Dict[str, Argument],\n    name: str,\n) -> Union[TRTTensor, Sequence[TRTTensor]]:\n    _LOGGER.debug(\n        f\"Evaluating {ConverterRegistry.qualified_name_or_str(target)} on object with name: {name}\"\n    )\n    return target(*args)\n", "path": "py/torch_tensorrt/dynamo/conversion/ops_evaluators.py"}], "after_files": [{"content": "import logging\nimport operator\nfrom typing import Dict, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom torch.fx.node import Argument, Node, Target\nfrom torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext\nfrom torch_tensorrt.dynamo.conversion._ConverterRegistry import (\n    ConverterRegistry,\n    dynamo_tensorrt_converter,\n)\nfrom torch_tensorrt.fx.types import TRTTensor\n\n_LOGGER: logging.Logger = logging.getLogger(__name__)\n\n\ndef getitem_validator(getitem_node: Node) -> bool:\n    from torch_tensorrt.dynamo.conversion._ConverterRegistry import DYNAMO_CONVERTERS\n\n    # Getitem nodes can only be converted if their parent node also can\n    return getitem_node.args[0] in DYNAMO_CONVERTERS\n\n\n# TODO: Subsequent evaluators should be registered here with their own validators\n@dynamo_tensorrt_converter(operator.getitem, capability_validator=getitem_validator)\n@dynamo_tensorrt_converter(torch.ops.aten.detach.default)\ndef generic_evaluator(\n    ctx: ConversionContext,\n    target: Target,\n    args: Tuple[Argument, ...],\n    kwargs: Dict[str, Argument],\n    name: str,\n) -> Union[TRTTensor, Sequence[TRTTensor]]:\n    _LOGGER.debug(\n        f\"Evaluating {ConverterRegistry.qualified_name_or_str(target)} on object with name: {name}\"\n    )\n    return target(*args)\n\n\n@dynamo_tensorrt_converter(torch.ops.aten.arange.start_step)\ndef aten_ops_arange_start_step(\n    ctx: ConversionContext,\n    target: Target,\n    args: Tuple[Argument, ...],\n    kwargs: Dict[str, Argument],\n    name: str,\n) -> Union[TRTTensor, Sequence[TRTTensor]]:\n    return np.arange(*args)\n", "path": "py/torch_tensorrt/dynamo/conversion/ops_evaluators.py"}]}