prompt: 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()
  
  ```
  
golden_diff: 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)
  
verification_info: {"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"}]}