prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Unclear comparisons in conditional operator implementation
  <!-- Edit the body of your new issue then click the ✓ "Create Issue" button in the top right of the editor. The first line will be the issue title. Assignees and Labels follow after a blank
  line. Leave an empty line before beginning the body of the issue. -->
  
  https://github.com/pytorch/TensorRT/blob/918e9832207d462c2a3aa42f9e7d3ab7aa7415aa/py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py#L68
  https://github.com/pytorch/TensorRT/blob/918e9832207d462c2a3aa42f9e7d3ab7aa7415aa/py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py#L75-L76
  https://github.com/pytorch/TensorRT/blob/918e9832207d462c2a3aa42f9e7d3ab7aa7415aa/py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py
  
  The above lines are being flagged by mypy as the following:
  
  ```sh
  py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py:68: error: Non-overlapping equality check (left operand type: "list[Any]", right operand type: "int")  [comparison-overlap]
  py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py:76: error: Non-overlapping equality check (left operand type: "list[Any]", right operand type: "int")  [comparison-overlap]
  py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py:98: error: Non-overlapping equality check (left operand type: "list[Any]", right operand type: "int")  [comparison-overlap]
  ```
  
  I cant really figure out what is being checked here but it is likely a bug.
  
  --- 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/impl/condition/ops.py`
  Content:
  ```
  1 from typing import Optional
  2 
  3 import torch
  4 from torch.fx.node import Target
  5 from torch_tensorrt.dynamo._SourceIR import SourceIR
  6 from torch_tensorrt.dynamo.conversion.converter_utils import broadcastable
  7 from torch_tensorrt.dynamo.conversion.impl.slice import expand
  8 from torch_tensorrt.fx.converters.converter_utils import (
  9     broadcast,
  10     get_trt_tensor,
  11     set_layer_name,
  12 )
  13 from torch_tensorrt.fx.types import TRTNetwork, TRTTensor
  14 
  15 import tensorrt as trt
  16 
  17 
  18 def where(
  19     network: TRTNetwork,
  20     target: Target,
  21     source_ir: Optional[SourceIR],
  22     name: str,
  23     input: TRTTensor,
  24     other: TRTTensor,
  25     condition: TRTTensor,
  26 ) -> TRTTensor:
  27     input_dim = len(tuple(input.shape))
  28     other_dim = len(tuple(other.shape))
  29     condition_dim = len(tuple(condition.shape))
  30 
  31     if type(input) != TRTTensor:
  32         assert type(input) is torch.Tensor, f"value {input} is not torch.Tensor!"
  33 
  34     if type(other) != TRTTensor:
  35         assert type(other) is torch.Tensor, f"value {other} is not torch.Tensor!"
  36 
  37     if not (broadcastable(input, other)):
  38         assert "The two torch tensors should be broadcastable"
  39 
  40     # get output shape
  41     # purpose of this is to bring input and other rank same as
  42     # output_shape to input it to the add_expand operation
  43     # condition will have dimension of either input or other
  44     input, other = broadcast(network, input, other, f"{name}_x", f"{name}_y")
  45     if len(tuple(condition.shape)) != len(tuple(input.shape)):
  46         condition, input = broadcast(
  47             network, condition, input, f"{name}_condition", f"{name}_x"
  48         )
  49 
  50     x_shape = list(input.shape)
  51     y_shape = list(other.shape)
  52     condition_shape = list(condition.shape)
  53     output_shape = list(torch.broadcast_shapes(condition_shape, x_shape, y_shape))
  54 
  55     # expand shape
  56     if type(condition) != TRTTensor:
  57         assert condition.dtype == torch.bool, "condition dtype is not bool"
  58         if condition_shape != output_shape:
  59             condition.expand(output_shape)
  60         condition = condition.to(torch.int32)
  61         condition_const = get_trt_tensor(network, condition, f"{name}_condition")
  62         condition_layer = network.add_identity(condition_const)
  63         condition_layer.set_output_type(0, trt.bool)
  64         set_layer_name(condition_layer, target, f"{name}_condition")
  65         condition_val = condition_layer.get_output(0)
  66     else:
  67         assert condition.dtype == trt.bool, "mask dtype is not bool!"
  68         if condition_shape != condition_dim:  # TODO: What is this checking?
  69             condition_val = expand(
  70                 network, target, source_ir, f"{name}_expand", condition, output_shape
  71             )
  72         else:
  73             condition_val = condition
  74 
  75     if type(input) != TRTTensor:
  76         if x_shape != input_dim:  # TODO: What is this checking?
  77             # special case where 1 element in input
  78             if len(input.shape) == 0:
  79                 input = input.unsqueeze(0)
  80             input = input.expand(output_shape)
  81         x_val = get_trt_tensor(network, input, f"{name}_x")
  82     else:
  83         x_val = input
  84         if x_shape != output_shape:
  85             x_val = expand(
  86                 network, target, source_ir, f"{name}_x_expand", input, output_shape
  87             )
  88 
  89     if type(other) != TRTTensor:
  90         if y_shape != output_shape:
  91             # special case where 1 element in other
  92             if len(other.shape) == 0:
  93                 other = other.unsqueeze(0)
  94             other = other.expand(output_shape)
  95         y_val = get_trt_tensor(network, other, f"{name}_y")
  96     else:
  97         y_val = other
  98         if y_shape != other_dim:  # TODO: What is this checking?
  99             y_val = expand(
  100                 network, target, source_ir, f"{name}_y_expand", y_val, output_shape
  101             )
  102 
  103     select_layer = network.add_select(condition_val, x_val, y_val)
  104 
  105     set_layer_name(select_layer, target, f"{name}_select")
  106 
  107     return select_layer.get_output(0)
  108
  ```
  --- 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/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
  @@ -65,7 +65,7 @@
           condition_val = condition_layer.get_output(0)
       else:
           assert condition.dtype == trt.bool, "mask dtype is not bool!"
  -        if condition_shape != condition_dim:  # TODO: What is this checking?
  +        if len(condition_shape) != condition_dim:
               condition_val = expand(
                   network, target, source_ir, f"{name}_expand", condition, output_shape
               )
  @@ -73,7 +73,7 @@
               condition_val = condition
   
       if type(input) != TRTTensor:
  -        if x_shape != input_dim:  # TODO: What is this checking?
  +        if x_shape != output_shape:
               # special case where 1 element in input
               if len(input.shape) == 0:
                   input = input.unsqueeze(0)
  @@ -95,7 +95,7 @@
           y_val = get_trt_tensor(network, other, f"{name}_y")
       else:
           y_val = other
  -        if y_shape != other_dim:  # TODO: What is this checking?
  +        if y_shape != output_shape:
               y_val = expand(
                   network, target, source_ir, f"{name}_y_expand", y_val, output_shape
               )
  
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@@ -65,7 +65,7 @@\n         condition_val = condition_layer.get_output(0)\n     else:\n         assert condition.dtype == trt.bool, \"mask dtype is not bool!\"\n-        if condition_shape != condition_dim:  # TODO: What is this checking?\n+        if len(condition_shape) != condition_dim:\n             condition_val = expand(\n                 network, target, source_ir, f\"{name}_expand\", condition, output_shape\n             )\n@@ -73,7 +73,7 @@\n             condition_val = condition\n \n     if type(input) != TRTTensor:\n-        if x_shape != input_dim:  # TODO: What is this checking?\n+        if x_shape != output_shape:\n             # special case where 1 element in input\n             if len(input.shape) == 0:\n                 input = input.unsqueeze(0)\n@@ -95,7 +95,7 @@\n         y_val = get_trt_tensor(network, other, f\"{name}_y\")\n     else:\n         y_val = other\n-        if y_shape != other_dim:  # TODO: What is this checking?\n+        if y_shape != output_shape:\n             y_val = expand(\n                 network, target, source_ir, f\"{name}_y_expand\", y_val, output_shape\n             )\n", "issue": "Unclear comparisons in conditional operator implementation\n<!-- Edit the body of your new issue then click the \u2713 \"Create Issue\" button in the top right of the editor. The first line will be the issue title. Assignees and Labels follow after a blank\nline. Leave an empty line before beginning the body of the issue. -->\n\nhttps://github.com/pytorch/TensorRT/blob/918e9832207d462c2a3aa42f9e7d3ab7aa7415aa/py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py#L68\nhttps://github.com/pytorch/TensorRT/blob/918e9832207d462c2a3aa42f9e7d3ab7aa7415aa/py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py#L75-L76\nhttps://github.com/pytorch/TensorRT/blob/918e9832207d462c2a3aa42f9e7d3ab7aa7415aa/py/torch_tensorrt/dynamo/conversion/impl/condition/ops.py\n\nThe above lines are being flagged by mypy as the following:\n\n```sh\npy/torch_tensorrt/dynamo/conversion/impl/condition/ops.py:68: error: Non-overlapping equality check (left operand type: \"list[Any]\", right operand type: \"int\")  [comparison-overlap]\npy/torch_tensorrt/dynamo/conversion/impl/condition/ops.py:76: error: Non-overlapping equality check (left operand type: \"list[Any]\", right operand type: \"int\")  [comparison-overlap]\npy/torch_tensorrt/dynamo/conversion/impl/condition/ops.py:98: error: Non-overlapping equality check (left operand type: \"list[Any]\", right operand type: \"int\")  [comparison-overlap]\n```\n\nI cant really figure out what is being checked here but it is likely a bug.\n", "before_files": [{"content": "from typing import Optional\n\nimport torch\nfrom torch.fx.node import Target\nfrom torch_tensorrt.dynamo._SourceIR import SourceIR\nfrom torch_tensorrt.dynamo.conversion.converter_utils import broadcastable\nfrom torch_tensorrt.dynamo.conversion.impl.slice import expand\nfrom torch_tensorrt.fx.converters.converter_utils import (\n    broadcast,\n    get_trt_tensor,\n    set_layer_name,\n)\nfrom torch_tensorrt.fx.types import TRTNetwork, TRTTensor\n\nimport tensorrt as trt\n\n\ndef where(\n    network: TRTNetwork,\n    target: Target,\n    source_ir: Optional[SourceIR],\n    name: str,\n    input: TRTTensor,\n    other: TRTTensor,\n    condition: TRTTensor,\n) -> TRTTensor:\n    input_dim = len(tuple(input.shape))\n    other_dim = len(tuple(other.shape))\n    condition_dim = len(tuple(condition.shape))\n\n    if type(input) != TRTTensor:\n        assert type(input) is torch.Tensor, f\"value {input} is not torch.Tensor!\"\n\n    if type(other) != TRTTensor:\n        assert type(other) is torch.Tensor, f\"value {other} is not torch.Tensor!\"\n\n    if not (broadcastable(input, other)):\n        assert \"The two torch tensors should be broadcastable\"\n\n    # get output shape\n    # purpose of this is to bring input and other rank same as\n    # output_shape to input it to the add_expand operation\n    # condition will have dimension of either input or other\n    input, other = broadcast(network, input, other, f\"{name}_x\", f\"{name}_y\")\n    if len(tuple(condition.shape)) != len(tuple(input.shape)):\n        condition, input = broadcast(\n            network, condition, input, f\"{name}_condition\", f\"{name}_x\"\n        )\n\n    x_shape = list(input.shape)\n    y_shape = list(other.shape)\n    condition_shape = list(condition.shape)\n    output_shape = list(torch.broadcast_shapes(condition_shape, x_shape, y_shape))\n\n    # expand shape\n    if type(condition) != TRTTensor:\n        assert condition.dtype == torch.bool, \"condition dtype is not bool\"\n        if condition_shape != output_shape:\n            condition.expand(output_shape)\n        condition = condition.to(torch.int32)\n        condition_const = get_trt_tensor(network, condition, f\"{name}_condition\")\n        condition_layer = network.add_identity(condition_const)\n        condition_layer.set_output_type(0, trt.bool)\n        set_layer_name(condition_layer, target, f\"{name}_condition\")\n        condition_val = condition_layer.get_output(0)\n    else:\n        assert condition.dtype == trt.bool, \"mask dtype is not bool!\"\n        if condition_shape != condition_dim:  # TODO: What is this checking?\n            condition_val = expand(\n                network, target, source_ir, f\"{name}_expand\", condition, output_shape\n            )\n        else:\n            condition_val = condition\n\n    if type(input) != TRTTensor:\n        if x_shape != input_dim:  # TODO: What is this checking?\n            # special case where 1 element in input\n            if len(input.shape) == 0:\n                input = input.unsqueeze(0)\n            input = input.expand(output_shape)\n        x_val = get_trt_tensor(network, input, f\"{name}_x\")\n    else:\n        x_val = input\n        if x_shape != output_shape:\n            x_val = expand(\n                network, target, source_ir, f\"{name}_x_expand\", input, output_shape\n            )\n\n    if type(other) != TRTTensor:\n        if y_shape != output_shape:\n            # special case where 1 element in other\n            if len(other.shape) == 0:\n                other = other.unsqueeze(0)\n            other = other.expand(output_shape)\n        y_val = get_trt_tensor(network, other, f\"{name}_y\")\n    else:\n        y_val = other\n        if y_shape != other_dim:  # TODO: What is this checking?\n            y_val = expand(\n                network, target, source_ir, f\"{name}_y_expand\", y_val, output_shape\n            )\n\n    select_layer = network.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"}], "after_files": [{"content": "from typing import Optional\n\nimport torch\nfrom torch.fx.node import Target\nfrom torch_tensorrt.dynamo._SourceIR import SourceIR\nfrom torch_tensorrt.dynamo.conversion.converter_utils import broadcastable\nfrom torch_tensorrt.dynamo.conversion.impl.slice import expand\nfrom torch_tensorrt.fx.converters.converter_utils import (\n    broadcast,\n    get_trt_tensor,\n    set_layer_name,\n)\nfrom torch_tensorrt.fx.types import TRTNetwork, TRTTensor\n\nimport tensorrt as trt\n\n\ndef where(\n    network: TRTNetwork,\n    target: Target,\n    source_ir: Optional[SourceIR],\n    name: str,\n    input: TRTTensor,\n    other: TRTTensor,\n    condition: TRTTensor,\n) -> TRTTensor:\n    input_dim = len(tuple(input.shape))\n    other_dim = len(tuple(other.shape))\n    condition_dim = len(tuple(condition.shape))\n\n    if type(input) != TRTTensor:\n        assert type(input) is torch.Tensor, f\"value {input} is not torch.Tensor!\"\n\n    if type(other) != TRTTensor:\n        assert type(other) is torch.Tensor, f\"value {other} is not torch.Tensor!\"\n\n    if not (broadcastable(input, other)):\n        assert \"The two torch tensors should be broadcastable\"\n\n    # get output shape\n    # purpose of this is to bring input and other rank same as\n    # output_shape to input it to the add_expand operation\n    # condition will have dimension of either input or other\n    input, other = broadcast(network, input, other, f\"{name}_x\", f\"{name}_y\")\n    if len(tuple(condition.shape)) != len(tuple(input.shape)):\n        condition, input = broadcast(\n            network, condition, input, f\"{name}_condition\", f\"{name}_x\"\n        )\n\n    x_shape = list(input.shape)\n    y_shape = list(other.shape)\n    condition_shape = list(condition.shape)\n    output_shape = list(torch.broadcast_shapes(condition_shape, x_shape, y_shape))\n\n    # expand shape\n    if type(condition) != TRTTensor:\n        assert condition.dtype == torch.bool, \"condition dtype is not bool\"\n        if condition_shape != output_shape:\n            condition.expand(output_shape)\n        condition = condition.to(torch.int32)\n        condition_const = get_trt_tensor(network, condition, f\"{name}_condition\")\n        condition_layer = network.add_identity(condition_const)\n        condition_layer.set_output_type(0, trt.bool)\n        set_layer_name(condition_layer, target, f\"{name}_condition\")\n        condition_val = condition_layer.get_output(0)\n    else:\n        assert condition.dtype == trt.bool, \"mask dtype is not bool!\"\n        if len(condition_shape) != condition_dim:\n            condition_val = expand(\n                network, target, source_ir, f\"{name}_expand\", condition, output_shape\n            )\n        else:\n            condition_val = condition\n\n    if type(input) != TRTTensor:\n        if x_shape != output_shape:\n            # special case where 1 element in input\n            if len(input.shape) == 0:\n                input = input.unsqueeze(0)\n            input = input.expand(output_shape)\n        x_val = get_trt_tensor(network, input, f\"{name}_x\")\n    else:\n        x_val = input\n        if x_shape != output_shape:\n            x_val = expand(\n                network, target, source_ir, f\"{name}_x_expand\", input, output_shape\n            )\n\n    if type(other) != TRTTensor:\n        if y_shape != output_shape:\n            # special case where 1 element in other\n            if len(other.shape) == 0:\n                other = other.unsqueeze(0)\n            other = other.expand(output_shape)\n        y_val = get_trt_tensor(network, other, f\"{name}_y\")\n    else:\n        y_val = other\n        if y_shape != output_shape:\n            y_val = expand(\n                network, target, source_ir, f\"{name}_y_expand\", y_val, output_shape\n            )\n\n    select_layer = network.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"}]}