prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  `aten.arange.start_step`
  
  
  </issue>
  <code>
  [start of py/torch_tensorrt/dynamo/conversion/ops_evaluators.py]
  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 of py/torch_tensorrt/dynamo/conversion/ops_evaluators.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/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"}]}