Expose IGridSampleLayer

[apbose]

Fixes #2202

[github-actions]

There are some changes that do not conform to Python style guidelines:

--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/converter_utils.py	2023-09-05 22:14:18.899998+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/converter_utils.py	2023-09-05 22:16:39.641414+00:00
@@ -16,36 +16,40 @@
from .._SourceIR import SourceIR
from .converter_registry import ConverterRegistry

_LOGGER: logging.Logger = logging.getLogger(__name__)

-#nearesr, linear, cubc
+
+# nearesr, linear, cubc
class GridSamplerInterpolation:
    def __init__(self):
        self.interpolator_mode = None
-    def __call__(self, interpolator_int):  
-        if(interpolator_int == 0) :
+
+    def __call__(self, interpolator_int):
+        if interpolator_int == 0:
            self.interpolator_mode = trt.InterpolationMode.NEAREST
-        elif(interpolator_int == 1) :
+        elif interpolator_int == 1:
            self.interpolator_mode = trt.InterpolationMode.LINEAR
-        elif(interpolator_int == 2) :
+        elif interpolator_int == 2:
            self.interpolator_mode = trt.InterpolationMode.CUBIC
        return self.interpolator_mode
-    
-
-#zeros, border, reflection
+
+
+# zeros, border, reflection
class GridSamplerPadding:
    def __init__(self):
        self.padding_mode = None
-    def __call__(self, padding_int):  
-        if(padding_int == 0) :
+
+    def __call__(self, padding_int):
+        if padding_int == 0:
            self.padding_mode = trt.SampleMode.kFILL
-        elif(padding_int == 1) :
+        elif padding_int == 1:
            self.padding_mode = trt.SampleMode.kCLAMP
-        elif(padding_int == 2) :
+        elif padding_int == 2:
            self.padding_mode = trt.SampleMode.kREFLECT
        return self.padding_mode
+

def get_node_name(node: torch.fx.Node) -> str:
    # nn_module_stack preserves the call stack of pytorch nn.modules
    # The call stack contains a detailed name of the module
    # which shows exactly where the module is located in the
--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/impl/grid.py	2023-09-05 22:14:18.903998+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/impl/grid.py	2023-09-05 22:16:39.839805+00:00
@@ -1,13 +1,17 @@
from typing import Optional

import torch
from torch.fx.node import Target
from torch_tensorrt.dynamo._SourceIR import SourceIR
-from torch_tensorrt.dynamo.conversion.converter_utils import GridSamplerInterpolation, GridSamplerPadding
+from torch_tensorrt.dynamo.conversion.converter_utils import (
+    GridSamplerInterpolation,
+    GridSamplerPadding,
+)
from torch_tensorrt.fx.converters.converter_utils import set_layer_name
from torch_tensorrt.fx.types import TRTNetwork, TRTTensor
+

def grid(
    network: TRTNetwork,
    target: Target,
    source_ir: Optional[SourceIR],
@@ -21,6 +25,6 @@
    grid_layer = network.add_grid_sample(input, grid)
    grid_layer.interpolation_mode = GridSamplerInterpolation(interpolation_mode)
    grid_layer.padding_mode = GridSamplerPadding(padding_mode)
    grid_layer.align_corners = align_corners
    set_layer_name(grid_layer, target, name + "_grid_layer", source_ir)
-    return grid_layer.get_output(0)
\ No newline at end of file
+    return grid_layer.get_output(0)
--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py	2023-09-05 22:14:18.899998+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py	2023-09-05 22:16:39.911918+00:00
@@ -163,11 +163,21 @@
    target: Target,
    args: Tuple[Argument, ...],
    kwargs: Dict[str, Argument],
    name: str,
) -> Union[TRTTensor, Sequence[TRTTensor]]:
-    return impl.grid.grid(network, target, SourceIR.ATEN, name, args[0], args[1], args[2], args[3], args[4])
+    return impl.grid.grid(
+        network,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args[1],
+        args[2],
+        args[3],
+        args[4],
+    )


@dynamo_tensorrt_converter(torch.ops.aten.relu.default)
def aten_ops_relu(
    network: TRTNetwork,
--- /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/conversion/test_grid_aten.py	2023-09-05 22:14:18.919998+00:00
+++ /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/conversion/test_grid_aten.py	2023-09-05 22:16:43.370582+00:00
@@ -4,35 +4,31 @@
from torch.testing._internal.common_utils import run_tests
from torch_tensorrt import Input
from parameterized import parameterized
from .harness import DispatchTestCase

+
class TestGridConverter(DispatchTestCase):
    @parameterized.expand(
        [
-            ("input_grid_interpolation_nearest_sample_fill", [5,5], [5,2], 0, 0),
-            ("input_grid_interpolation_nearest_sample_clamp", [5,5], [5,2], 0, 1),
-            ("input_grid_interpolation_nearest_sample_reflect", [5,5], [5,2], 0, 2),
-            ("input_grid_interpolation_linear_sample_fill", [5,5], [5,2], 1, 0),
-            ("input_grid_interpolation_linear_sample_clamp", [5,5], [5,2], 1, 1),
-            ("input_grid_interpolation_linear_sample_reflect", [5,5], [5,2], 1, 2),
-            ("input_grid_interpolation_cubic_sample_fill", [5,5], [5,2], 2, 0),
-            ("input_grid_interpolation_cubic_sample_clamp", [5,5], [5,2], 2, 1),
-            ("input_grid_interpolation_cubic_sample_reflect", [5,5], [5,2], 2, 2),
+            ("input_grid_interpolation_nearest_sample_fill", [5, 5], [5, 2], 0, 0),
+            ("input_grid_interpolation_nearest_sample_clamp", [5, 5], [5, 2], 0, 1),
+            ("input_grid_interpolation_nearest_sample_reflect", [5, 5], [5, 2], 0, 2),
+            ("input_grid_interpolation_linear_sample_fill", [5, 5], [5, 2], 1, 0),
+            ("input_grid_interpolation_linear_sample_clamp", [5, 5], [5, 2], 1, 1),
+            ("input_grid_interpolation_linear_sample_reflect", [5, 5], [5, 2], 1, 2),
+            ("input_grid_interpolation_cubic_sample_fill", [5, 5], [5, 2], 2, 0),
+            ("input_grid_interpolation_cubic_sample_clamp", [5, 5], [5, 2], 2, 1),
+            ("input_grid_interpolation_cubic_sample_reflect", [5, 5], [5, 2], 2, 2),
        ]
    )
-    def test_grid(self,_, input_shape, dim_shape, interpolation, sample):
+    def test_grid(self, _, input_shape, dim_shape, interpolation, sample):
        class TestModule(nn.Module):
            def forward(self, x):
                input = torch.randn(10).reshape(input_shape)
                grid = torch.randint(-1, 1, dim_shape)
                return nn.functional.grid(input, grid, interpolation, sample)

        inputs = [torch.randn(1, 10)]
-        self.run_test(TestModule(), inputs, expected_ops={torch.ops.aten.grid_sampler.out})
-
-
-
-
-    
-
-    
\ No newline at end of file
+        self.run_test(
+            TestModule(), inputs, expected_ops={torch.ops.aten.grid_sampler.out}
+        )

[github-actions]

Code conforms to C++ style guidelines

[github-actions]

Code conforms to C++ style guidelines

[github-actions]

There are some changes that do not conform to Python style guidelines:

--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/converter_utils.py	2023-10-06 17:56:01.641986+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/converter_utils.py	2023-10-06 17:59:18.326542+00:00
@@ -21,36 +21,40 @@
)
from torch_tensorrt.fx.types import TRTDataType, TRTTensor

_LOGGER: logging.Logger = logging.getLogger(__name__)

-#nearesr, linear, cubc
+
+# nearesr, linear, cubc
class GridSamplerInterpolation:
    def __init__(self):
        self.interpolator_mode = None
-    def __call__(self, interpolator_int):  
-        if(interpolator_int == 0) :
+
+    def __call__(self, interpolator_int):
+        if interpolator_int == 0:
            self.interpolator_mode = trt.InterpolationMode.NEAREST
-        elif(interpolator_int == 1) :
+        elif interpolator_int == 1:
            self.interpolator_mode = trt.InterpolationMode.LINEAR
-        elif(interpolator_int == 2) :
+        elif interpolator_int == 2:
            self.interpolator_mode = trt.InterpolationMode.CUBIC
        return self.interpolator_mode
-    
-
-#zeros, border, reflection
+
+
+# zeros, border, reflection
class GridSamplerPadding:
    def __init__(self):
        self.padding_mode = None
-    def __call__(self, padding_int):  
-        if(padding_int == 0) :
+
+    def __call__(self, padding_int):
+        if padding_int == 0:
            self.padding_mode = trt.SampleMode.kFILL
-        elif(padding_int == 1) :
+        elif padding_int == 1:
            self.padding_mode = trt.SampleMode.kCLAMP
-        elif(padding_int == 2) :
+        elif padding_int == 2:
            self.padding_mode = trt.SampleMode.kREFLECT
        return self.padding_mode
+

def get_node_name(node: torch.fx.Node) -> str:
    # nn_module_stack preserves the call stack of pytorch nn.modules
    # The call stack contains a detailed name of the module
    # which shows exactly where the module is located in the
--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/impl/grid.py	2023-10-06 17:56:01.641986+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/impl/grid.py	2023-10-06 17:59:18.704188+00:00
@@ -1,13 +1,17 @@
from typing import Optional

import torch
from torch.fx.node import Target
from torch_tensorrt.dynamo._SourceIR import SourceIR
-from torch_tensorrt.dynamo.conversion.converter_utils import GridSamplerInterpolation, GridSamplerPadding
+from torch_tensorrt.dynamo.conversion.converter_utils import (
+    GridSamplerInterpolation,
+    GridSamplerPadding,
+)
from torch_tensorrt.fx.converters.converter_utils import set_layer_name
from torch_tensorrt.fx.types import TRTNetwork, TRTTensor
+

def grid(
    network: TRTNetwork,
    target: Target,
    source_ir: Optional[SourceIR],
@@ -21,6 +25,6 @@
    grid_layer = network.add_grid_sample(input, grid)
    grid_layer.interpolation_mode = GridSamplerInterpolation(interpolation_mode)
    grid_layer.padding_mode = GridSamplerPadding(padding_mode)
    grid_layer.align_corners = align_corners
    set_layer_name(grid_layer, target, name + "_grid_layer", source_ir)
-    return grid_layer.get_output(0)
\ No newline at end of file
+    return grid_layer.get_output(0)
--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py	2023-10-06 17:56:01.641986+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py	2023-10-06 17:59:18.888738+00:00
@@ -132,11 +132,13 @@
    target: Target,
    args: Tuple[Argument, ...],
    kwargs: Dict[str, Argument],
    name: str,
) -> Union[TRTTensor, Sequence[TRTTensor]]:
-    return impl.grid.grid(ctx, target, SourceIR.ATEN, name, args[0], args[1], args[2], args[3], args[4])
+    return impl.grid.grid(
+        ctx, target, SourceIR.ATEN, name, args[0], args[1], args[2], args[3], args[4]
+    )


@dynamo_tensorrt_converter(torch.ops.aten.relu.default)
def aten_ops_relu(
    ctx: ConversionContext,
--- /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/conversion/test_grid_aten.py	2023-10-06 17:56:01.669986+00:00
+++ /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/conversion/test_grid_aten.py	2023-10-06 17:59:24.740152+00:00
@@ -4,35 +4,31 @@
from torch.testing._internal.common_utils import run_tests
from torch_tensorrt import Input
from parameterized import parameterized
from .harness import DispatchTestCase

+
class TestGridConverter(DispatchTestCase):
    @parameterized.expand(
        [
-            ("input_grid_interpolation_nearest_sample_fill", [5,5], [5,2], 0, 0),
-            ("input_grid_interpolation_nearest_sample_clamp", [5,5], [5,2], 0, 1),
-            ("input_grid_interpolation_nearest_sample_reflect", [5,5], [5,2], 0, 2),
-            ("input_grid_interpolation_linear_sample_fill", [5,5], [5,2], 1, 0),
-            ("input_grid_interpolation_linear_sample_clamp", [5,5], [5,2], 1, 1),
-            ("input_grid_interpolation_linear_sample_reflect", [5,5], [5,2], 1, 2),
-            ("input_grid_interpolation_cubic_sample_fill", [5,5], [5,2], 2, 0),
-            ("input_grid_interpolation_cubic_sample_clamp", [5,5], [5,2], 2, 1),
-            ("input_grid_interpolation_cubic_sample_reflect", [5,5], [5,2], 2, 2),
+            ("input_grid_interpolation_nearest_sample_fill", [5, 5], [5, 2], 0, 0),
+            ("input_grid_interpolation_nearest_sample_clamp", [5, 5], [5, 2], 0, 1),
+            ("input_grid_interpolation_nearest_sample_reflect", [5, 5], [5, 2], 0, 2),
+            ("input_grid_interpolation_linear_sample_fill", [5, 5], [5, 2], 1, 0),
+            ("input_grid_interpolation_linear_sample_clamp", [5, 5], [5, 2], 1, 1),
+            ("input_grid_interpolation_linear_sample_reflect", [5, 5], [5, 2], 1, 2),
+            ("input_grid_interpolation_cubic_sample_fill", [5, 5], [5, 2], 2, 0),
+            ("input_grid_interpolation_cubic_sample_clamp", [5, 5], [5, 2], 2, 1),
+            ("input_grid_interpolation_cubic_sample_reflect", [5, 5], [5, 2], 2, 2),
        ]
    )
-    def test_grid(self,_, input_shape, dim_shape, interpolation, sample):
+    def test_grid(self, _, input_shape, dim_shape, interpolation, sample):
        class TestModule(nn.Module):
            def forward(self, x):
                input = torch.randn(10).reshape(input_shape)
                grid = torch.randint(-1, 1, dim_shape)
                return nn.functional.grid(input, grid, interpolation, sample)

        inputs = [torch.randn(1, 10)]
-        self.run_test(TestModule(), inputs, expected_ops={torch.ops.aten.grid_sampler.out})
-
-
-
-
-    
-
-    
\ No newline at end of file
+        self.run_test(
+            TestModule(), inputs, expected_ops={torch.ops.aten.grid_sampler.out}
+        )

[github-actions]

Code conforms to C++ style guidelines

[github-actions]

There are some changes that do not conform to Python style guidelines:

--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/converter_utils.py	2023-10-12 20:23:11.175226+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/converter_utils.py	2023-10-12 20:26:13.990588+00:00
@@ -21,36 +21,40 @@
)
from torch_tensorrt.fx.types import TRTDataType, TRTTensor

_LOGGER: logging.Logger = logging.getLogger(__name__)

-#nearesr, linear, cubc
+
+# nearesr, linear, cubc
class GridSamplerInterpolation:
    def __init__(self):
        self.interpolator_mode = None
-    def __call__(self, interpolator_int):  
-        if(interpolator_int == 0) :
+
+    def __call__(self, interpolator_int):
+        if interpolator_int == 0:
            self.interpolator_mode = trt.InterpolationMode.NEAREST
-        elif(interpolator_int == 1) :
+        elif interpolator_int == 1:
            self.interpolator_mode = trt.InterpolationMode.LINEAR
-        elif(interpolator_int == 2) :
+        elif interpolator_int == 2:
            self.interpolator_mode = trt.InterpolationMode.CUBIC
        return self.interpolator_mode
-    
-
-#zeros, border, reflection
+
+
+# zeros, border, reflection
class GridSamplerPadding:
    def __init__(self):
        self.padding_mode = None
-    def __call__(self, padding_int):  
-        if(padding_int == 0) :
+
+    def __call__(self, padding_int):
+        if padding_int == 0:
            self.padding_mode = trt.SampleMode.kFILL
-        elif(padding_int == 1) :
+        elif padding_int == 1:
            self.padding_mode = trt.SampleMode.kCLAMP
-        elif(padding_int == 2) :
+        elif padding_int == 2:
            self.padding_mode = trt.SampleMode.kREFLECT
        return self.padding_mode
+

def get_node_name(node: torch.fx.Node) -> str:
    # nn_module_stack preserves the call stack of pytorch nn.modules
    # The call stack contains a detailed name of the module
    # which shows exactly where the module is located in the
--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/impl/grid.py	2023-10-12 20:23:11.175226+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/impl/grid.py	2023-10-12 20:26:14.342525+00:00
@@ -1,13 +1,17 @@
from typing import Optional

import torch
from torch.fx.node import Target
from torch_tensorrt.dynamo._SourceIR import SourceIR
-from torch_tensorrt.dynamo.conversion.converter_utils import GridSamplerInterpolation, GridSamplerPadding
+from torch_tensorrt.dynamo.conversion.converter_utils import (
+    GridSamplerInterpolation,
+    GridSamplerPadding,
+)
from torch_tensorrt.fx.converters.converter_utils import set_layer_name
from torch_tensorrt.fx.types import TRTNetwork, TRTTensor
+

def grid(
    network: TRTNetwork,
    target: Target,
    source_ir: Optional[SourceIR],
@@ -21,6 +25,6 @@
    grid_layer = network.add_grid_sample(input, grid)
    grid_layer.interpolation_mode = GridSamplerInterpolation(interpolation_mode)
    grid_layer.padding_mode = GridSamplerPadding(padding_mode)
    grid_layer.align_corners = align_corners
    set_layer_name(grid_layer, target, name + "_grid_layer", source_ir)
-    return grid_layer.get_output(0)
\ No newline at end of file
+    return grid_layer.get_output(0)
--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py	2023-10-12 20:23:11.175226+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py	2023-10-12 20:26:14.706447+00:00
@@ -256,11 +256,13 @@
    target: Target,
    args: Tuple[Argument, ...],
    kwargs: Dict[str, Argument],
    name: str,
) -> Union[TRTTensor, Sequence[TRTTensor]]:
-    return impl.grid.grid(ctx, target, SourceIR.ATEN, name, args[0], args[1], args[2], args[3], args[4])
+    return impl.grid.grid(
+        ctx, target, SourceIR.ATEN, name, args[0], args[1], args[2], args[3], args[4]
+    )


@dynamo_tensorrt_converter(torch.ops.aten.relu.default)
def aten_ops_relu(
    ctx: ConversionContext,
--- /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/conversion/test_grid_aten.py	2023-10-12 20:23:11.199228+00:00
+++ /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/conversion/test_grid_aten.py	2023-10-12 20:26:19.877166+00:00
@@ -4,35 +4,31 @@
from torch.testing._internal.common_utils import run_tests
from torch_tensorrt import Input
from parameterized import parameterized
from .harness import DispatchTestCase

+
class TestGridConverter(DispatchTestCase):
    @parameterized.expand(
        [
-            ("input_grid_interpolation_nearest_sample_fill", [5,5], [5,2], 0, 0),
-            ("input_grid_interpolation_nearest_sample_clamp", [5,5], [5,2], 0, 1),
-            ("input_grid_interpolation_nearest_sample_reflect", [5,5], [5,2], 0, 2),
-            ("input_grid_interpolation_linear_sample_fill", [5,5], [5,2], 1, 0),
-            ("input_grid_interpolation_linear_sample_clamp", [5,5], [5,2], 1, 1),
-            ("input_grid_interpolation_linear_sample_reflect", [5,5], [5,2], 1, 2),
-            ("input_grid_interpolation_cubic_sample_fill", [5,5], [5,2], 2, 0),
-            ("input_grid_interpolation_cubic_sample_clamp", [5,5], [5,2], 2, 1),
-            ("input_grid_interpolation_cubic_sample_reflect", [5,5], [5,2], 2, 2),
+            ("input_grid_interpolation_nearest_sample_fill", [5, 5], [5, 2], 0, 0),
+            ("input_grid_interpolation_nearest_sample_clamp", [5, 5], [5, 2], 0, 1),
+            ("input_grid_interpolation_nearest_sample_reflect", [5, 5], [5, 2], 0, 2),
+            ("input_grid_interpolation_linear_sample_fill", [5, 5], [5, 2], 1, 0),
+            ("input_grid_interpolation_linear_sample_clamp", [5, 5], [5, 2], 1, 1),
+            ("input_grid_interpolation_linear_sample_reflect", [5, 5], [5, 2], 1, 2),
+            ("input_grid_interpolation_cubic_sample_fill", [5, 5], [5, 2], 2, 0),
+            ("input_grid_interpolation_cubic_sample_clamp", [5, 5], [5, 2], 2, 1),
+            ("input_grid_interpolation_cubic_sample_reflect", [5, 5], [5, 2], 2, 2),
        ]
    )
-    def test_grid(self,_, input_shape, dim_shape, interpolation, sample):
+    def test_grid(self, _, input_shape, dim_shape, interpolation, sample):
        class TestModule(nn.Module):
            def forward(self, x):
                input = torch.randn(10).reshape(input_shape)
                grid = torch.randint(-1, 1, dim_shape)
                return nn.functional.grid(input, grid, interpolation, sample)

        inputs = [torch.randn(1, 10)]
-        self.run_test(TestModule(), inputs, expected_ops={torch.ops.aten.grid_sampler.out})
-
-
-
-
-    
-
-    
\ No newline at end of file
+        self.run_test(
+            TestModule(), inputs, expected_ops={torch.ops.aten.grid_sampler.out}
+        )

[github-actions]

There are some changes that do not conform to Python style guidelines:

--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/converter_utils.py	2023-10-13 00:17:54.909933+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/converter_utils.py	2023-10-13 00:20:33.526856+00:00
@@ -21,36 +21,40 @@
)
from torch_tensorrt.fx.types import TRTDataType, TRTTensor

_LOGGER: logging.Logger = logging.getLogger(__name__)

-#nearesr, linear, cubc
+
+# nearesr, linear, cubc
class GridSamplerInterpolation:
    def __init__(self):
        self.interpolator_mode = None
-    def __call__(self, interpolator_int):  
-        if(interpolator_int == 0) :
+
+    def __call__(self, interpolator_int):
+        if interpolator_int == 0:
            self.interpolator_mode = trt.InterpolationMode.NEAREST
-        elif(interpolator_int == 1) :
+        elif interpolator_int == 1:
            self.interpolator_mode = trt.InterpolationMode.LINEAR
-        elif(interpolator_int == 2) :
+        elif interpolator_int == 2:
            self.interpolator_mode = trt.InterpolationMode.CUBIC
        return self.interpolator_mode
-    
-
-#zeros, border, reflection
+
+
+# zeros, border, reflection
class GridSamplerPadding:
    def __init__(self):
        self.padding_mode = None
-    def __call__(self, padding_int):  
-        if(padding_int == 0) :
+
+    def __call__(self, padding_int):
+        if padding_int == 0:
            self.padding_mode = trt.SampleMode.kFILL
-        elif(padding_int == 1) :
+        elif padding_int == 1:
            self.padding_mode = trt.SampleMode.kCLAMP
-        elif(padding_int == 2) :
+        elif padding_int == 2:
            self.padding_mode = trt.SampleMode.kREFLECT
        return self.padding_mode
+

def get_node_name(node: torch.fx.Node) -> str:
    # nn_module_stack preserves the call stack of pytorch nn.modules
    # The call stack contains a detailed name of the module
    # which shows exactly where the module is located in the
--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/impl/grid.py	2023-10-13 00:17:54.909933+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/impl/grid.py	2023-10-13 00:20:33.917971+00:00
@@ -3,13 +3,18 @@
import torch
import tensorrt as trt
from torch.fx.node import Target
from torch_tensorrt.dynamo._SourceIR import SourceIR
from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
-from torch_tensorrt.dynamo.conversion.converter_utils import cast_trt_tensor, GridSamplerInterpolation, GridSamplerSampling
+from torch_tensorrt.dynamo.conversion.converter_utils import (
+    cast_trt_tensor,
+    GridSamplerInterpolation,
+    GridSamplerSampling,
+)
from torch_tensorrt.fx.converters.converter_utils import set_layer_name
from torch_tensorrt.fx.types import TRTNetwork, TRTTensor
+

def grid(
    ctx: ConversionContext,
    target: Target,
    source_ir: Optional[SourceIR],
@@ -20,20 +25,20 @@
    padding_mode: int,
    align_corners: bool,
    output_mask: Optional[Sequence[bool]] = None,
) -> TRTTensor:
    grid_layer = ctx.net.add_grid_sample(input, grid)
-    interpolation_mode_trt = GridSamplerInterpolation()    
+    interpolation_mode_trt = GridSamplerInterpolation()
    grid_layer.interpolation_mode = interpolation_mode_trt(interpolation_mode)
    sample_mode_trt = GridSamplerSampling()
-    grid_layer.sample_mode = sample_mode_trt(padding_mode) 
+    grid_layer.sample_mode = sample_mode_trt(padding_mode)
    grid_layer.align_corners = align_corners
    set_layer_name(grid_layer, target, name + "_grid_layer", source_ir)
-    if(output_mask is None):
+    if output_mask is None:
        return grid_layer.get_output(0)
    else:
-        if(output_mask[0] and output_mask[1]):
+        if output_mask[0] and output_mask[1]:
            return (grid_layer.get_output(0), None)
-        elif(output_mask[0]):
+        elif output_mask[0]:
            return grid_layer.get_output(0)
        else:
-            return None
\ No newline at end of file
+            return None
--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py	2023-10-13 00:17:54.909933+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py	2023-10-13 00:20:34.077902+00:00
@@ -262,21 +262,20 @@
    args: Tuple[Argument, ...],
    kwargs: Dict[str, Argument],
    name: str,
) -> Union[TRTTensor, Sequence[TRTTensor]]:
    return impl.grid.grid(
-        ctx, 
-        target, 
-        SourceIR.ATEN, 
-        name, 
-        input=args[0], 
-        grid=args[1], 
-        interpolation_mode=args[2], 
-        padding_mode=args[3], 
+        ctx,
+        target,
+        SourceIR.ATEN,
+        name,
+        input=args[0],
+        grid=args[1],
+        interpolation_mode=args[2],
+        padding_mode=args[3],
        align_corners=args_bounds_check(args, 4, True),
        output_mask=args_bounds_check(args, 5, None),
-
    )


@dynamo_tensorrt_converter(torch.ops.aten.relu.default)
def aten_ops_relu(
--- /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/conversion/test_grid_aten.py	2023-10-13 00:17:54.929933+00:00
+++ /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/conversion/test_grid_aten.py	2023-10-13 00:20:38.093945+00:00
@@ -4,35 +4,86 @@
from torch.testing._internal.common_utils import run_tests
from torch_tensorrt import Input
from parameterized import parameterized
from harness import DispatchTestCase

+
class TestGridConverter(DispatchTestCase):
    @parameterized.expand(
        [
-            ("input_grid_interpolation_nearest_sample_fill", [1,1,5,5], [1,5,2,2], 0, 0),
-            ("input_grid_interpolation_nearest_sample_clamp", [1,1,5,5], [1,5,2,2], 0, 1),
-            ("input_grid_interpolation_nearest_sample_reflect", [1,1,5,5], [1,5,2,2], 0, 2),
-            ("input_grid_interpolation_linear_sample_fill", [1,1,5,5], [1,5,2,2], 1, 0),
-            ("input_grid_interpolation_linear_sample_clamp", [1,1,5,5], [1,5,2,2], 1, 1),
-            ("input_grid_interpolation_linear_sample_reflect", [1,1,5,5], [1,5,2,2], 1, 2),
-            ("input_grid_interpolation_cubic_sample_fill", [1,1,5,5], [1,5,2,2], 2, 0),
-            ("input_grid_interpolation_cubic_sample_clamp", [1,1,5,5], [1,5,2,2], 2, 1),
-            ("input_grid_interpolation_cubic_sample_reflect", [1,1,5,5], [1,5,2,2], 2, 2),
+            (
+                "input_grid_interpolation_nearest_sample_fill",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                0,
+                0,
+            ),
+            (
+                "input_grid_interpolation_nearest_sample_clamp",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                0,
+                1,
+            ),
+            (
+                "input_grid_interpolation_nearest_sample_reflect",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                0,
+                2,
+            ),
+            (
+                "input_grid_interpolation_linear_sample_fill",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                1,
+                0,
+            ),
+            (
+                "input_grid_interpolation_linear_sample_clamp",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                1,
+                1,
+            ),
+            (
+                "input_grid_interpolation_linear_sample_reflect",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                1,
+                2,
+            ),
+            (
+                "input_grid_interpolation_cubic_sample_fill",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                2,
+                0,
+            ),
+            (
+                "input_grid_interpolation_cubic_sample_clamp",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                2,
+                1,
+            ),
+            (
+                "input_grid_interpolation_cubic_sample_reflect",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                2,
+                2,
+            ),
        ]
    )
    def test_grid(self, _, input_shape, dim_shape, interpolation, sample):
        class TestModule(nn.Module):
-            def forward(self, x): 
+            def forward(self, x):
                grid = torch.randint(-1, 1, dim_shape, dtype=torch.float32)
                return torch.ops.aten.grid_sampler(x, grid, interpolation, sample, True)
-        inputs = [torch.randn(input_shape, dtype = torch.float32)]
+
+        inputs = [torch.randn(input_shape, dtype=torch.float32)]
        self.run_test(TestModule(), inputs)
+

if __name__ == "__main__":
    run_tests()
-
-
-
-    
-
-    
\ No newline at end of file

[github-actions]

Code conforms to C++ style guidelines

[github-actions]

There are some changes that do not conform to Python style guidelines:

--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/converter_utils.py	2023-10-13 00:22:45.467171+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/converter_utils.py	2023-10-13 00:25:24.662061+00:00
@@ -21,36 +21,40 @@
)
from torch_tensorrt.fx.types import TRTDataType, TRTTensor

_LOGGER: logging.Logger = logging.getLogger(__name__)

-#nearesr, linear, cubc
+
+# nearesr, linear, cubc
class GridSamplerInterpolation:
    def __init__(self):
        self.interpolator_mode = None
-    def __call__(self, interpolator_int):  
-        if(interpolator_int == 0) :
+
+    def __call__(self, interpolator_int):
+        if interpolator_int == 0:
            self.interpolator_mode = trt.InterpolationMode.NEAREST
-        elif(interpolator_int == 1) :
+        elif interpolator_int == 1:
            self.interpolator_mode = trt.InterpolationMode.LINEAR
-        elif(interpolator_int == 2) :
+        elif interpolator_int == 2:
            self.interpolator_mode = trt.InterpolationMode.CUBIC
        return self.interpolator_mode
-    
-
-#zeros, border, reflection
+
+
+# zeros, border, reflection
class GridSamplerPadding:
    def __init__(self):
        self.padding_mode = None
-    def __call__(self, padding_int):  
-        if(padding_int == 0) :
+
+    def __call__(self, padding_int):
+        if padding_int == 0:
            self.padding_mode = trt.SampleMode.kFILL
-        elif(padding_int == 1) :
+        elif padding_int == 1:
            self.padding_mode = trt.SampleMode.kCLAMP
-        elif(padding_int == 2) :
+        elif padding_int == 2:
            self.padding_mode = trt.SampleMode.kREFLECT
        return self.padding_mode
+

def get_node_name(node: torch.fx.Node) -> str:
    # nn_module_stack preserves the call stack of pytorch nn.modules
    # The call stack contains a detailed name of the module
    # which shows exactly where the module is located in the
--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/impl/grid.py	2023-10-13 00:22:45.467171+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/impl/grid.py	2023-10-13 00:25:25.012634+00:00
@@ -3,13 +3,18 @@
import torch
import tensorrt as trt
from torch.fx.node import Target
from torch_tensorrt.dynamo._SourceIR import SourceIR
from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
-from torch_tensorrt.dynamo.conversion.converter_utils import cast_trt_tensor, GridSamplerInterpolation, GridSamplerSampling
+from torch_tensorrt.dynamo.conversion.converter_utils import (
+    cast_trt_tensor,
+    GridSamplerInterpolation,
+    GridSamplerSampling,
+)
from torch_tensorrt.fx.converters.converter_utils import set_layer_name
from torch_tensorrt.fx.types import TRTNetwork, TRTTensor
+

def grid(
    ctx: ConversionContext,
    target: Target,
    source_ir: Optional[SourceIR],
@@ -20,20 +25,20 @@
    padding_mode: int,
    align_corners: bool,
    output_mask: Optional[Sequence[bool]] = None,
) -> TRTTensor:
    grid_layer = ctx.net.add_grid_sample(input, grid)
-    interpolation_mode_trt = GridSamplerInterpolation()    
+    interpolation_mode_trt = GridSamplerInterpolation()
    grid_layer.interpolation_mode = interpolation_mode_trt(interpolation_mode)
    sample_mode_trt = GridSamplerSampling()
-    grid_layer.sample_mode = sample_mode_trt(padding_mode) 
+    grid_layer.sample_mode = sample_mode_trt(padding_mode)
    grid_layer.align_corners = align_corners
    set_layer_name(grid_layer, target, name + "_grid_layer", source_ir)
-    if(output_mask is None):
+    if output_mask is None:
        return grid_layer.get_output(0)
    else:
-        if(output_mask[0] and output_mask[1]):
+        if output_mask[0] and output_mask[1]:
            return (grid_layer.get_output(0), None)
-        elif(output_mask[0]):
+        elif output_mask[0]:
            return grid_layer.get_output(0)
        else:
-            return None
\ No newline at end of file
+            return None
--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py	2023-10-13 00:22:45.467171+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py	2023-10-13 00:25:25.209460+00:00
@@ -262,21 +262,20 @@
    args: Tuple[Argument, ...],
    kwargs: Dict[str, Argument],
    name: str,
) -> Union[TRTTensor, Sequence[TRTTensor]]:
    return impl.grid.grid(
-        ctx, 
-        target, 
-        SourceIR.ATEN, 
-        name, 
-        input=args[0], 
-        grid=args[1], 
-        interpolation_mode=args[2], 
-        padding_mode=args[3], 
+        ctx,
+        target,
+        SourceIR.ATEN,
+        name,
+        input=args[0],
+        grid=args[1],
+        interpolation_mode=args[2],
+        padding_mode=args[3],
        align_corners=args_bounds_check(args, 4, True),
        output_mask=args_bounds_check(args, 5, None),
-
    )


@dynamo_tensorrt_converter(torch.ops.aten.relu.default)
def aten_ops_relu(
--- /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/conversion/test_grid_aten.py	2023-10-13 00:22:45.487171+00:00
+++ /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/conversion/test_grid_aten.py	2023-10-13 00:25:28.842495+00:00
@@ -4,35 +4,86 @@
from torch.testing._internal.common_utils import run_tests
from torch_tensorrt import Input
from parameterized import parameterized
from harness import DispatchTestCase

+
class TestGridConverter(DispatchTestCase):
    @parameterized.expand(
        [
-            ("input_grid_interpolation_nearest_sample_fill", [1,1,5,5], [1,5,2,2], 0, 0),
-            ("input_grid_interpolation_nearest_sample_clamp", [1,1,5,5], [1,5,2,2], 0, 1),
-            ("input_grid_interpolation_nearest_sample_reflect", [1,1,5,5], [1,5,2,2], 0, 2),
-            ("input_grid_interpolation_linear_sample_fill", [1,1,5,5], [1,5,2,2], 1, 0),
-            ("input_grid_interpolation_linear_sample_clamp", [1,1,5,5], [1,5,2,2], 1, 1),
-            ("input_grid_interpolation_linear_sample_reflect", [1,1,5,5], [1,5,2,2], 1, 2),
-            ("input_grid_interpolation_cubic_sample_fill", [1,1,5,5], [1,5,2,2], 2, 0),
-            ("input_grid_interpolation_cubic_sample_clamp", [1,1,5,5], [1,5,2,2], 2, 1),
-            ("input_grid_interpolation_cubic_sample_reflect", [1,1,5,5], [1,5,2,2], 2, 2),
+            (
+                "input_grid_interpolation_nearest_sample_fill",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                0,
+                0,
+            ),
+            (
+                "input_grid_interpolation_nearest_sample_clamp",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                0,
+                1,
+            ),
+            (
+                "input_grid_interpolation_nearest_sample_reflect",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                0,
+                2,
+            ),
+            (
+                "input_grid_interpolation_linear_sample_fill",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                1,
+                0,
+            ),
+            (
+                "input_grid_interpolation_linear_sample_clamp",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                1,
+                1,
+            ),
+            (
+                "input_grid_interpolation_linear_sample_reflect",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                1,
+                2,
+            ),
+            (
+                "input_grid_interpolation_cubic_sample_fill",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                2,
+                0,
+            ),
+            (
+                "input_grid_interpolation_cubic_sample_clamp",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                2,
+                1,
+            ),
+            (
+                "input_grid_interpolation_cubic_sample_reflect",
+                [1, 1, 5, 5],
+                [1, 5, 2, 2],
+                2,
+                2,
+            ),
        ]
    )
    def test_grid(self, _, input_shape, dim_shape, interpolation, sample):
        class TestModule(nn.Module):
-            def forward(self, x): 
+            def forward(self, x):
                grid = torch.randint(-1, 1, dim_shape, dtype=torch.float32)
                return torch.ops.aten.grid_sampler(x, grid, interpolation, sample, True)
-        inputs = [torch.randn(input_shape, dtype = torch.float32)]
+
+        inputs = [torch.randn(input_shape, dtype=torch.float32)]
        self.run_test(TestModule(), inputs)
+

if __name__ == "__main__":
    run_tests()
-
-
-
-    
-
-    
\ No newline at end of file

[github-actions]

Code conforms to C++ style guidelines

[github-actions]

Code conforms to C++ style guidelines

[github-actions]

There are some changes that do not conform to Python style guidelines:

--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py	2023-10-13 00:25:52.876877+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py	2023-10-13 00:28:28.778380+00:00
@@ -262,21 +262,20 @@
    args: Tuple[Argument, ...],
    kwargs: Dict[str, Argument],
    name: str,
) -> Union[TRTTensor, Sequence[TRTTensor]]:
    return impl.grid.grid(
-        ctx, 
-        target, 
-        SourceIR.ATEN, 
-        name, 
-        input=args[0], 
-        grid=args[1], 
-        interpolation_mode=args[2], 
-        padding_mode=args[3], 
+        ctx,
+        target,
+        SourceIR.ATEN,
+        name,
+        input=args[0],
+        grid=args[1],
+        interpolation_mode=args[2],
+        padding_mode=args[3],
        align_corners=args_bounds_check(args, 4, True),
        output_mask=args_bounds_check(args, 5, None),
-
    )


@dynamo_tensorrt_converter(torch.ops.aten.relu.default)
def aten_ops_relu(

[github-actions]

Code conforms to C++ style guidelines

[github-actions]

There are some changes that do not conform to Python style guidelines:

--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py	2023-10-13 00:51:36.892575+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py	2023-10-13 00:54:31.161981+00:00
@@ -262,21 +262,20 @@
    args: Tuple[Argument, ...],
    kwargs: Dict[str, Argument],
    name: str,
) -> Union[TRTTensor, Sequence[TRTTensor]]:
    return impl.grid.grid(
-        ctx, 
-        target, 
-        SourceIR.ATEN, 
-        name, 
-        input=args[0], 
-        grid=args[1], 
-        interpolation_mode=args[2], 
-        padding_mode=args[3], 
+        ctx,
+        target,
+        SourceIR.ATEN,
+        name,
+        input=args[0],
+        grid=args[1],
+        interpolation_mode=args[2],
+        padding_mode=args[3],
        align_corners=args_bounds_check(args, 4, True),
        output_mask=args_bounds_check(args, 5, None),
-
    )


@dynamo_tensorrt_converter(torch.ops.aten.relu.default)
def aten_ops_relu(

[gs-olive]

Add # type: ignore[misc] to these

[gs-olive]

Remove _backward implementations. Consider changing the decorator stack to:

@dynamo_tensorrt_converter(torch.ops.aten.grid_sampler.default)  # type: ignore[misc]
@dynamo_tensorrt_converter(torch.ops.aten.grid_sampler_2d.default)  # type: ignore[misc]
@dynamo_tensorrt_converter(torch.ops.aten.grid_sampler_3d.default)  # type: ignore[misc]

[apbose]

I will add the # type: ignore[misc]. I read online that this indicates that mypy would ignore this. So how do we know where this is to be mentioned?
Also regarding the .out, I see that the target in the test is torch.ops.aten.grid_sampler, so should default be mentioned then? Because just mentioning

@dynamo_tensorrt_converter(torch.ops.aten.grid_sampler.default)  # type: ignore[misc]
@dynamo_tensorrt_converter(torch.ops.aten.grid_sampler_2d.default)  # type: ignore[misc]
@dynamo_tensorrt_converter(torch.ops.aten.grid_sampler_3d.default)  # type: ignore[misc]

Shows the following error

py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py", line 335,
 in call_function
    raise UnsupportedOperatorException(
torch_tensorrt.dynamo.conversion._TRTInterpreter.UnsupportedOperatorException: Conversion of function torch._ops.aten.PyCapsule.grid_sampler not
currently supported!

[gs-olive]

Regarding the # type: ignore[misc], generally mypy linting will indicate an error for untyped decorators. Our dynamo_tensorrt_converter is not untyped, but for some reason the error persists. For now, we just keep the ignore on all decorators in this file.

On the second issue, I think you can remove the .out variant, but change the @dynamo_tensorrt_converter(torch.ops.aten.grid_sampler.default) back to just @dynamo_tensorrt_converter(torch.ops.aten.grid_sampler). That should address the error

[gs-olive]

This can be removed once the out variants are removed from the decorator list

[apbose]

Do you mean the output_mask argument? As far as I understood, the .backward would have the output_mask argument.

[gs-olive]

Yes, I meant the output_mask argument. I don't think we should ever encounter an argument of the backward variety since the backend is strictly forward/inference-only

[gs-olive]

I think this could just go in the grid.py file, since it is very specific to the grid operations.

[gs-olive]

Consider using a dictionary for this - could be grid_layer.interpolation_mode = Dictionary.get(interpolation_mode, None)

[gs-olive]

Bring up one level and make all of these elif statements

[github-actions]

Code conforms to C++ style guidelines

[github-actions]

There are some changes that do not conform to Python style guidelines:

--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py	2023-10-20 00:36:43.890962+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py	2023-10-20 00:39:35.534806+00:00
@@ -262,21 +262,20 @@
    args: Tuple[Argument, ...],
    kwargs: Dict[str, Argument],
    name: str,
) -> Union[TRTTensor, Sequence[TRTTensor]]:
    return impl.grid.grid(
-        ctx, 
-        target, 
-        SourceIR.ATEN, 
-        name, 
-        input=args[0], 
-        grid=args[1], 
-        interpolation_mode=args[2], 
-        padding_mode=args[3], 
+        ctx,
+        target,
+        SourceIR.ATEN,
+        name,
+        input=args[0],
+        grid=args[1],
+        interpolation_mode=args[2],
+        padding_mode=args[3],
        align_corners=args_bounds_check(args, 4, True),
        output_mask=args_bounds_check(args, 5, None),
-
    )


@dynamo_tensorrt_converter(torch.ops.aten.relu.default)
def aten_ops_relu(

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Code conforms to C++ style guidelines

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Code conforms to Python style guidelines

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Code conforms to Python style guidelines

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Code conforms to C++ style guidelines

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Code conforms to Python style guidelines

[github-actions]

Code conforms to C++ style guidelines

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Code conforms to C++ style guidelines

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Code conforms to Python style guidelines

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Code conforms to Python style guidelines

[github-actions]

Code conforms to C++ style guidelines

[github-actions]

Code conforms to C++ style guidelines

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Code conforms to Python style guidelines

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Code conforms to C++ style guidelines

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Code conforms to Python style guidelines

[gs-olive]

Looks good to me!