prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Expose ISelectLayer in dynamo.conversion.impl
  
  
  </issue>
  <code>
  [start of py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py]
  1 from typing import Optional, Union
  2 
  3 import numpy as np
  4 import tensorrt as trt
  5 import torch
  6 from torch.fx.node import Target
  7 from torch_tensorrt.dynamo._SourceIR import SourceIR
  8 from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
  9 from torch_tensorrt.dynamo.conversion.converter_utils import (
  10     broadcastable,
  11     get_trt_tensor,
  12 )
  13 from torch_tensorrt.dynamo.conversion.impl.slice import expand
  14 from torch_tensorrt.fx.converters.converter_utils import set_layer_name
  15 from torch_tensorrt.fx.types import TRTTensor
  16 
  17 
  18 def where(
  19     ctx: ConversionContext,
  20     target: Target,
  21     source_ir: Optional[SourceIR],
  22     name: str,
  23     input: Union[TRTTensor, np.ndarray, torch.Tensor],
  24     other: Union[TRTTensor, np.ndarray, torch.Tensor],
  25     condition: Union[TRTTensor, np.ndarray, torch.Tensor],
  26 ) -> TRTTensor:
  27     if not (broadcastable(input, other)):
  28         assert "The two torch tensors should be broadcastable"
  29 
  30     x_shape = list(input.shape)
  31     y_shape = list(other.shape)
  32     condition_shape = list(condition.shape)
  33 
  34     output_shape = list(torch.broadcast_shapes(condition_shape, x_shape, y_shape))
  35 
  36     # expand shape
  37     if not isinstance(condition, TRTTensor):
  38         assert condition.dtype in (torch.bool, np.bool_), "condition dtype is not bool"
  39         if condition_shape != output_shape:
  40             condition = (
  41                 condition.expand(output_shape)
  42                 if isinstance(condition, torch.Tensor)
  43                 else np.broadcast_to(condition, output_shape)
  44             )
  45         condition_val = get_trt_tensor(ctx, condition, f"{name}_condition")
  46     else:
  47         assert condition.dtype == trt.bool, "mask dtype is not bool!"
  48         if condition_shape != output_shape:
  49             condition_val = expand(
  50                 ctx, target, source_ir, f"{name}_expand", condition, output_shape
  51             )
  52         else:
  53             condition_val = condition
  54 
  55     if not isinstance(input, TRTTensor):
  56         if x_shape != output_shape:
  57             # special case where 1 element in input
  58             if len(input.shape) == 0:
  59                 input = (
  60                     input.unsqueeze(0)
  61                     if isinstance(input, torch.Tensor)
  62                     else np.expand_dims(input, axis=0)
  63                 )
  64             input = (
  65                 input.expand(output_shape)
  66                 if isinstance(input, torch.Tensor)
  67                 else np.broadcast_to(input, output_shape)
  68             )
  69         x_val = get_trt_tensor(ctx, input, f"{name}_x")
  70     else:
  71         x_val = input
  72         if x_shape != output_shape:
  73             x_val = expand(
  74                 ctx, target, source_ir, f"{name}_x_expand", input, output_shape
  75             )
  76 
  77     if not isinstance(other, TRTTensor):
  78         if y_shape != output_shape:
  79             # special case where 1 element in other
  80             if len(other.shape) == 0:
  81                 other = (
  82                     other.unsqueeze(0)
  83                     if isinstance(other, torch.Tensor)
  84                     else np.expand_dims(other, axis=0)
  85                 )
  86             other = (
  87                 other.expand(output_shape)
  88                 if isinstance(other, torch.Tensor)
  89                 else np.broadcast_to(other, output_shape)
  90             )
  91         y_val = get_trt_tensor(ctx, other, f"{name}_y")
  92     else:
  93         y_val = other
  94         if y_shape != output_shape:
  95             y_val = expand(
  96                 ctx, target, source_ir, f"{name}_y_expand", y_val, output_shape
  97             )
  98 
  99     select_layer = ctx.net.add_select(condition_val, x_val, y_val)
  100 
  101     set_layer_name(select_layer, target, f"{name}_select")
  102 
  103     return select_layer.get_output(0)
  104
  [end of py/torch_tensorrt/dynamo/conversion/impl/condition/ops.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/impl/condition/ops.py b/py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py
  --- a/py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py
  +++ b/py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py
  @@ -96,8 +96,18 @@
                   ctx, target, source_ir, f"{name}_y_expand", y_val, output_shape
               )
   
  -    select_layer = ctx.net.add_select(condition_val, x_val, y_val)
  +    return select(ctx, target, source_ir, name, x_val, y_val, condition_val)
   
  -    set_layer_name(select_layer, target, f"{name}_select")
   
  +def select(
  +    ctx: ConversionContext,
  +    target: Target,
  +    source_ir: Optional[SourceIR],
  +    name: str,
  +    input: TRTTensor,
  +    other: TRTTensor,
  +    condition: TRTTensor,
  +) -> TRTTensor:
  +    select_layer = ctx.net.add_select(condition, input, other)
  +    set_layer_name(select_layer, target, name + "_select", source_ir)
       return select_layer.get_output(0)
  
verification_info: {"golden_diff": "diff --git a/py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py b/py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py\n--- a/py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py\n+++ b/py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py\n@@ -96,8 +96,18 @@\n                 ctx, target, source_ir, f\"{name}_y_expand\", y_val, output_shape\n             )\n \n-    select_layer = ctx.net.add_select(condition_val, x_val, y_val)\n+    return select(ctx, target, source_ir, name, x_val, y_val, condition_val)\n \n-    set_layer_name(select_layer, target, f\"{name}_select\")\n \n+def select(\n+    ctx: ConversionContext,\n+    target: Target,\n+    source_ir: Optional[SourceIR],\n+    name: str,\n+    input: TRTTensor,\n+    other: TRTTensor,\n+    condition: TRTTensor,\n+) -> TRTTensor:\n+    select_layer = ctx.net.add_select(condition, input, other)\n+    set_layer_name(select_layer, target, name + \"_select\", source_ir)\n     return select_layer.get_output(0)\n", "issue": "Expose ISelectLayer in dynamo.conversion.impl\n\n", "before_files": [{"content": "from typing import Optional, Union\n\nimport numpy as np\nimport tensorrt as trt\nimport torch\nfrom torch.fx.node import Target\nfrom torch_tensorrt.dynamo._SourceIR import SourceIR\nfrom torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext\nfrom torch_tensorrt.dynamo.conversion.converter_utils import (\n    broadcastable,\n    get_trt_tensor,\n)\nfrom torch_tensorrt.dynamo.conversion.impl.slice import expand\nfrom torch_tensorrt.fx.converters.converter_utils import set_layer_name\nfrom torch_tensorrt.fx.types import TRTTensor\n\n\ndef where(\n    ctx: ConversionContext,\n    target: Target,\n    source_ir: Optional[SourceIR],\n    name: str,\n    input: Union[TRTTensor, np.ndarray, torch.Tensor],\n    other: Union[TRTTensor, np.ndarray, torch.Tensor],\n    condition: Union[TRTTensor, np.ndarray, torch.Tensor],\n) -> TRTTensor:\n    if not (broadcastable(input, other)):\n        assert \"The two torch tensors should be broadcastable\"\n\n    x_shape = list(input.shape)\n    y_shape = list(other.shape)\n    condition_shape = list(condition.shape)\n\n    output_shape = list(torch.broadcast_shapes(condition_shape, x_shape, y_shape))\n\n    # expand shape\n    if not isinstance(condition, TRTTensor):\n        assert condition.dtype in (torch.bool, np.bool_), \"condition dtype is not bool\"\n        if condition_shape != output_shape:\n            condition = (\n                condition.expand(output_shape)\n                if isinstance(condition, torch.Tensor)\n                else np.broadcast_to(condition, output_shape)\n            )\n        condition_val = get_trt_tensor(ctx, condition, f\"{name}_condition\")\n    else:\n        assert condition.dtype == trt.bool, \"mask dtype is not bool!\"\n        if condition_shape != output_shape:\n            condition_val = expand(\n                ctx, target, source_ir, f\"{name}_expand\", condition, output_shape\n            )\n        else:\n            condition_val = condition\n\n    if not isinstance(input, TRTTensor):\n        if x_shape != output_shape:\n            # special case where 1 element in input\n            if len(input.shape) == 0:\n                input = (\n                    input.unsqueeze(0)\n                    if isinstance(input, torch.Tensor)\n                    else np.expand_dims(input, axis=0)\n                )\n            input = (\n                input.expand(output_shape)\n                if isinstance(input, torch.Tensor)\n                else np.broadcast_to(input, output_shape)\n            )\n        x_val = get_trt_tensor(ctx, input, f\"{name}_x\")\n    else:\n        x_val = input\n        if x_shape != output_shape:\n            x_val = expand(\n                ctx, target, source_ir, f\"{name}_x_expand\", input, output_shape\n            )\n\n    if not isinstance(other, TRTTensor):\n        if y_shape != output_shape:\n            # special case where 1 element in other\n            if len(other.shape) == 0:\n                other = (\n                    other.unsqueeze(0)\n                    if isinstance(other, torch.Tensor)\n                    else np.expand_dims(other, axis=0)\n                )\n            other = (\n                other.expand(output_shape)\n                if isinstance(other, torch.Tensor)\n                else np.broadcast_to(other, output_shape)\n            )\n        y_val = get_trt_tensor(ctx, other, f\"{name}_y\")\n    else:\n        y_val = other\n        if y_shape != output_shape:\n            y_val = expand(\n                ctx, target, source_ir, f\"{name}_y_expand\", y_val, output_shape\n            )\n\n    select_layer = ctx.net.add_select(condition_val, x_val, y_val)\n\n    set_layer_name(select_layer, target, f\"{name}_select\")\n\n    return select_layer.get_output(0)\n", "path": "py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py"}]}